{"input": "GxVAEs : Two Joint VAEs Generate Hit Molecules from Gene Expression Profiles .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 0, "end": 6}, {"text": "VAEs", "start": 19, "end": 23}]}, "relations": {"part of": [{"head": {"text": "VAEs", "start": 19, "end": 23}, "tail": {"text": "GxVAEs", "start": 0, "end": 6}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The de novo generation of hit-like molecules that show bioactivity and drug-likeness is an important task in computer-aided drug discovery .", "output": {"entities": {"task": [{"text": "de novo generation", "start": 4, "end": 22}, {"text": "computer-aided drug discovery", "start": 109, "end": 138}]}, "relations": {"subtask of": [{"head": {"text": "de novo generation", "start": 4, "end": 22}, "tail": {"text": "computer-aided drug discovery", "start": 109, "end": 138}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Although artificial intelligence can generate molecules with desired chemical properties , most previous studies have ignored the influence of disease-related cellular environments .", "output": {"entities": {"method": [{"text": "artificial intelligence", "start": 9, "end": 32}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This study proposes a novel deep generative model called GxVAEs to generate hit-like molecules from gene expression profiles by leveraging two joint variational autoencoders ( VAEs ) .", "output": {"entities": {"method": [{"text": "deep generative model", "start": 28, "end": 49}, {"text": "GxVAEs", "start": 57, "end": 63}, {"text": "variational autoencoders", "start": 149, "end": 173}, {"text": "VAEs", "start": 176, "end": 180}]}, "relations": {"subclass of": [{"head": {"text": "GxVAEs", "start": 57, "end": 63}, "tail": {"text": "deep generative model", "start": 28, "end": 49}}], "part of": [{"head": {"text": "variational autoencoders", "start": 149, "end": 173}, "tail": {"text": "GxVAEs", "start": 57, "end": 63}}], "synonym of": [{"head": {"text": "VAEs", "start": 176, "end": 180}, "tail": {"text": "variational autoencoders", "start": 149, "end": 173}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The first VAE , ProfileVAE , extracts latent features from gene expression profiles .", "output": {"entities": {"method": [{"text": "VAE", "start": 10, "end": 13}, {"text": "ProfileVAE", "start": 16, "end": 26}]}, "relations": {"subclass of": [{"head": {"text": "ProfileVAE", "start": 16, "end": 26}, "tail": {"text": "VAE", "start": 10, "end": 13}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The extracted features serve as the conditions that guide the second VAE , which is called MolVAE , in generating hit-like molecules .", "output": {"entities": {"method": [{"text": "VAE", "start": 69, "end": 72}, {"text": "MolVAE", "start": 91, "end": 97}]}, "relations": {"subclass of": [{"head": {"text": "MolVAE", "start": 91, "end": 97}, "tail": {"text": "VAE", "start": 69, "end": 72}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "GxVAEs bridge the gap between molecular generation and the cellular environment in a biological system , and produce molecules that are biologically meaningful in the context of specific diseases .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 0, "end": 6}], "task": [{"text": "molecular generation", "start": 30, "end": 50}]}, "relations": {"used for": [{"head": {"text": "GxVAEs", "start": 0, "end": 6}, "tail": {"text": "molecular generation", "start": 30, "end": 50}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Experiments and case studies on the generation of therapeutic molecules show that GxVAEs outperforms current state-of-the-art baselines and yield hit-like molecules with potential bioactivity and drug-like properties .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 82, "end": 88}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Hit identification , which involves discovering hit molecules with the desired bioactivity and therapeutic effects within an infinite chemical space , is a critical challenge in drug discovery ( Dobson et al . 2004 ) .", "output": {"entities": {"task": [{"text": "Hit identification", "start": 0, "end": 18}, {"text": "drug discovery", "start": 178, "end": 192}]}, "relations": {"subtask of": [{"head": {"text": "Hit identification", "start": 0, "end": 18}, "tail": {"text": "drug discovery", "start": 178, "end": 192}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Traditionally , hit identification campaigns have been conducted using experimental approaches such as high-throughput screening ( HTS ) ( Hertzberg and Pope 2000 ) .", "output": {"entities": {"task": [{"text": "hit identification", "start": 16, "end": 34}], "method": [{"text": "high-throughput screening", "start": 103, "end": 128}, {"text": "HTS", "start": 131, "end": 134}]}, "relations": {"used for": [{"head": {"text": "high-throughput screening", "start": 103, "end": 128}, "tail": {"text": "hit identification", "start": 16, "end": 34}}], "synonym of": [{"head": {"text": "HTS", "start": 131, "end": 134}, "tail": {"text": "high-throughput screening", "start": 103, "end": 128}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "HTS provides a valuable methodology for identifying hits with desired bioactivity ( Scannell et al . 2022 ) .", "output": {"entities": {"method": [{"text": "HTS", "start": 0, "end": 3}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , the hit rate observed in HTS campaigns tends to be relatively low , which results in many molecules that are inactive or that hold less promise being screened out ( Rahman et al . 2022 ) .", "output": {"entities": {"method": [{"text": "HTS", "start": 35, "end": 38}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , experimental approaches to drug discovery typically rely on laborintensive and time-consuming processes .", "output": {"entities": {"task": [{"text": "drug discovery", "start": 42, "end": 56}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "De novo molecular generation using artificial intelligence ( AI ) techniques has emerged as a promising approach for computer-aided drug discovery .", "output": {"entities": {"task": [{"text": "novo molecular generation", "start": 3, "end": 28}, {"text": "computer-aided drug discovery", "start": 117, "end": 146}], "method": [{"text": "artificial intelligence", "start": 35, "end": 58}, {"text": "AI", "start": 61, "end": 63}]}, "relations": {"used for": [{"head": {"text": "artificial intelligence", "start": 35, "end": 58}, "tail": {"text": "novo molecular generation", "start": 3, "end": 28}}, {"head": {"text": "artificial intelligence", "start": 35, "end": 58}, "tail": {"text": "computer-aided drug discovery", "start": 117, "end": 146}}], "synonym of": [{"head": {"text": "AI", "start": 61, "end": 63}, "tail": {"text": "artificial intelligence", "start": 35, "end": 58}}], "subtask of": [{"head": {"text": "novo molecular generation", "start": 3, "end": 28}, "tail": {"text": "computer-aided drug discovery", "start": 117, "end": 146}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The power of deep learning and computational chemistry enables the generation of new molecules with desired bioactivity for specific therapeutic targets .", "output": {"entities": {"method": [{"text": "deep learning", "start": 13, "end": 26}, {"text": "computational chemistry", "start": 31, "end": 54}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Deep generative models , such as generative adversarial networks ( GANs ) ( Guimaraes et al . 2017 ; De Cao and Kipf 2018 ; Li et al . 2022 ) and variational autoencoders ( VAEs ) ( Oliveira , Da Silva , and Quiles 2022 ; Dollar et al . 2021 ; Kusner , Paige , and Hernández-Lobato 2017 ; Jin , Barzilay , and Jaakkola 2018 ) accelerate the drug discovery process by employing computational models to generate molecules having a specific bioactivity .", "output": {"entities": {"method": [{"text": "Deep generative models", "start": 0, "end": 22}, {"text": "generative adversarial networks", "start": 33, "end": 64}, {"text": "GANs", "start": 67, "end": 71}, {"text": "variational autoencoders", "start": 146, "end": 170}, {"text": "VAEs", "start": 173, "end": 177}], "task": [{"text": "drug discovery", "start": 341, "end": 355}]}, "relations": {"subclass of": [{"head": {"text": "generative adversarial networks", "start": 33, "end": 64}, "tail": {"text": "Deep generative models", "start": 0, "end": 22}}, {"head": {"text": "variational autoencoders", "start": 146, "end": 170}, "tail": {"text": "Deep generative models", "start": 0, "end": 22}}], "synonym of": [{"head": {"text": "GANs", "start": 67, "end": 71}, "tail": {"text": "generative adversarial networks", "start": 33, "end": 64}}, {"head": {"text": "VAEs", "start": 173, "end": 177}, "tail": {"text": "variational autoencoders", "start": 146, "end": 170}}], "used for": [{"head": {"text": "variational autoencoders", "start": 146, "end": 170}, "tail": {"text": "drug discovery", "start": 341, "end": 355}}, {"head": {"text": "generative adversarial networks", "start": 33, "end": 64}, "tail": {"text": "drug discovery", "start": 341, "end": 355}}, {"head": {"text": "Deep generative models", "start": 0, "end": 22}, "tail": {"text": "drug discovery", "start": 341, "end": 355}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By capturing the essential features of molecules , deep generative models can produce new molecules with similar structures and related properties .", "output": {"entities": {"method": [{"text": "deep generative models", "start": 51, "end": 73}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Most AI-driven molecular generation models focus on the generation of new molecules with improved chemical properties of interest .", "output": {"entities": {"method": [{"text": "AI-driven molecular generation models", "start": 5, "end": 42}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "While omics-based approaches to drug discovery offer promising opportunities , it is essential to understand their limitations ( Kang , Ko , and Mersha 2022 ) .", "output": {"entities": {"method": [{"text": "omics-based approaches", "start": 6, "end": 28}], "task": [{"text": "drug discovery", "start": 32, "end": 46}]}, "relations": {"used for": [{"head": {"text": "omics-based approaches", "start": 6, "end": 28}, "tail": {"text": "drug discovery", "start": 32, "end": 46}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Interpretation of omics data in the context of drug discovery also requires a deep understanding of the underlying biological and disease mechanisms .", "output": {"entities": {"task": [{"text": "drug discovery", "start": 47, "end": 61}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this study , we proposed GxVAEs to generate hit-like molecules from gene expression profiles using two joint VAEs ( i.e . , ProfileVAE and MolVAE ) .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 28, "end": 34}, {"text": "VAEs", "start": 112, "end": 116}, {"text": "ProfileVAE", "start": 127, "end": 137}, {"text": "MolVAE", "start": 142, "end": 148}]}, "relations": {"part of": [{"head": {"text": "VAEs", "start": 112, "end": 116}, "tail": {"text": "GxVAEs", "start": 28, "end": 34}}, {"head": {"text": "ProfileVAE", "start": 127, "end": 137}, "tail": {"text": "GxVAEs", "start": 28, "end": 34}}, {"head": {"text": "MolVAE", "start": 142, "end": 148}, "tail": {"text": "GxVAEs", "start": 28, "end": 34}}], "subclass of": [{"head": {"text": "ProfileVAE", "start": 127, "end": 137}, "tail": {"text": "VAEs", "start": 112, "end": 116}}, {"head": {"text": "MolVAE", "start": 142, "end": 148}, "tail": {"text": "VAEs", "start": 112, "end": 116}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "First , ProfileVAE functions as a feature extractor to extract latent features from gene expression profiles .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 8, "end": 18}, {"text": "feature extractor", "start": 34, "end": 51}]}, "relations": {"subclass of": [{"head": {"text": "ProfileVAE", "start": 8, "end": 18}, "tail": {"text": "feature extractor", "start": 34, "end": 51}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The extracted features are then used as conditions to guide MolVAE in generating hit-like molecules .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 60, "end": 66}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "GxVAEs bridges the gap between molecular generation and the cellular environment of a biological system , making the produced molecules more biologically meaningful in the context of specific diseases .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 0, "end": 6}], "task": [{"text": "molecular generation", "start": 31, "end": 51}]}, "relations": {"used for": [{"head": {"text": "GxVAEs", "start": 0, "end": 6}, "tail": {"text": "molecular generation", "start": 31, "end": 51}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The main contributions of this study are summarized as follows : • Hit-like molecular generation that considered the cellular environment : This study considers the influence of the cellular environment on specific diseases during the molecular generation process . • A simple but effective model architecture : The combination of two VAEs successfully generated hit-like molecules from gene expression profiles . • Superior performance over state-of-the-art ( SOTA ) models : Experiments and case studies demonstrate that the proposed GxVAEs outperform the current SOTA models for the same objectives .", "output": {"entities": {"task": [{"text": "Hit-like molecular generation", "start": 67, "end": 96}, {"text": "molecular generation", "start": 235, "end": 255}], "method": [{"text": "VAEs", "start": 335, "end": 339}, {"text": "GxVAEs", "start": 536, "end": 542}]}, "relations": {"used for": [{"head": {"text": "VAEs", "start": 335, "end": 339}, "tail": {"text": "Hit-like molecular generation", "start": 67, "end": 96}}, {"head": {"text": "GxVAEs", "start": 536, "end": 542}, "tail": {"text": "Hit-like molecular generation", "start": 67, "end": 96}}, {"head": {"text": "VAEs", "start": 335, "end": 339}, "tail": {"text": "molecular generation", "start": 235, "end": 255}}, {"head": {"text": "GxVAEs", "start": 536, "end": 542}, "tail": {"text": "molecular generation", "start": 235, "end": 255}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "De novo molecular generation aims to produce new molecules with specific chemical properties .", "output": {"entities": {"task": [{"text": "De novo molecular generation", "start": 0, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Generally , the data structures used for de novo molecular generation are molecular graphs ( Manolopoulos and Fowler 1992 ) and simplified molecular input line entry system ( SMILES ) strings ( Weininger 1988 ) .", "output": {"entities": {"task": [{"text": "de novo molecular generation", "start": 41, "end": 69}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "TransVAE ( Dollar et al . 2021 ) and Gram-marVAE ( Kusner , Paige , and Hernández-Lobato 2017 ) are two deep generative models that produce molecules from SMILES strings using VAEs .", "output": {"entities": {"method": [{"text": "TransVAE", "start": 0, "end": 8}, {"text": "Gram-marVAE", "start": 37, "end": 48}, {"text": "deep generative models", "start": 104, "end": 126}, {"text": "VAEs", "start": 176, "end": 180}]}, "relations": {"part of": [{"head": {"text": "VAEs", "start": 176, "end": 180}, "tail": {"text": "TransVAE", "start": 0, "end": 8}}, {"head": {"text": "VAEs", "start": 176, "end": 180}, "tail": {"text": "Gram-marVAE", "start": 37, "end": 48}}], "subclass of": [{"head": {"text": "Gram-marVAE", "start": 37, "end": 48}, "tail": {"text": "deep generative models", "start": 104, "end": 126}}, {"head": {"text": "TransVAE", "start": 0, "end": 8}, "tail": {"text": "deep generative models", "start": 104, "end": 126}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "JTVAE ( Jin , Barzilay , and Jaakkola 2018 ) uses a node tree to generate molecules from a molecular graph .", "output": {"entities": {"method": [{"text": "JTVAE", "start": 0, "end": 5}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It first creates a tree-structured scaffold on a substructure and then combines the tree-structured scaffold into a molecule using a graphical message-passing network ( Dai , Dai , and Song 2016 ) .", "output": {"entities": {"method": [{"text": "graphical message-passing network", "start": 133, "end": 166}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MolGAN ( De Cao and Kipf 2018 ) and TransORGAN ( Li et al . 2022 ) are discrete GANs that use reinforcement learning to generate molecular graphs and SMILES strings , respectively .", "output": {"entities": {"method": [{"text": "MolGAN", "start": 0, "end": 6}, {"text": "TransORGAN", "start": 36, "end": 46}, {"text": "discrete GANs", "start": 71, "end": 84}, {"text": "reinforcement learning", "start": 94, "end": 116}]}, "relations": {"used for": [{"head": {"text": "reinforcement learning", "start": 94, "end": 116}, "tail": {"text": "MolGAN", "start": 0, "end": 6}}, {"head": {"text": "reinforcement learning", "start": 94, "end": 116}, "tail": {"text": "TransORGAN", "start": 36, "end": 46}}], "subclass of": [{"head": {"text": "TransORGAN", "start": 36, "end": 46}, "tail": {"text": "discrete GANs", "start": 71, "end": 84}}, {"head": {"text": "MolGAN", "start": 0, "end": 6}, "tail": {"text": "discrete GANs", "start": 71, "end": 84}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "With the development of omics data analysis , especially gene expression profiles used in drug discovery ( Turanli et al . 2018 ; Chen et al . 2020 ) , chemists have begun to use comprehensive biological response information to generate therapeutic molecules for the treatment of specific diseases .", "output": {"entities": {"task": [{"text": "drug discovery", "start": 90, "end": 104}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "ExpressionGAN ( Méndez-Lucio et al . 2020 ) and TRI-OMPHE ( Kaitoh and Yamanishi 2021 ) are the SOTA deep generative models that are most relevant to our study on generating hit-like molecules for target proteins with no prior annotation of the target training molecules .", "output": {"entities": {"method": [{"text": "ExpressionGAN", "start": 0, "end": 13}, {"text": "TRI-OMPHE", "start": 48, "end": 57}, {"text": "deep generative models", "start": 101, "end": 123}]}, "relations": {"subclass of": [{"head": {"text": "TRI-OMPHE", "start": 48, "end": 57}, "tail": {"text": "deep generative models", "start": 101, "end": 123}}, {"head": {"text": "ExpressionGAN", "start": 0, "end": 13}, "tail": {"text": "deep generative models", "start": 101, "end": 123}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Expres-sionGAN is a GAN model that bridges systems biology and molecular design to produce molecules with a high probability of inducing the desired transcriptome features automatically from gene expression profiles .", "output": {"entities": {"method": [{"text": "Expres-sionGAN", "start": 0, "end": 14}, {"text": "GAN", "start": 20, "end": 23}]}, "relations": {"subclass of": [{"head": {"text": "Expres-sionGAN", "start": 0, "end": 14}, "tail": {"text": "GAN", "start": 20, "end": 23}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "TRIOMPHE first calculates the correlation of ligand-target interactions between the chemically induced transcriptome profiles of cellular responses to molecular treatment and the transcriptome profiles of gene perturbations that reflect cellular responses to gene knockdown or gene overexpression of target proteins .", "output": {"entities": {"method": [{"text": "TRIOMPHE", "start": 0, "end": 8}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Next , a VAE was employed to generate a new molecule with the desired transcriptome profile .", "output": {"entities": {"method": [{"text": "VAE", "start": 9, "end": 12}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , ExpressionGAN generates hit-like molecules with low validity ( > 8.5% ) , and its ability to reproduce known ligands is limited .", "output": {"entities": {"method": [{"text": "ExpressionGAN", "start": 10, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , TRIOMPHE only utilizes gene expression profiles in correlation calculations , whose VAE is not involved in molecular generation .", "output": {"entities": {"method": [{"text": "TRIOMPHE", "start": 15, "end": 23}, {"text": "VAE", "start": 99, "end": 102}]}, "relations": {"part of": [{"head": {"text": "VAE", "start": 99, "end": 102}, "tail": {"text": "TRIOMPHE", "start": 15, "end": 23}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To address these issues , GxVAEs were proposed in this study .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 26, "end": 32}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By bridging the gap between the cellular environment and the generation of ther - The Thirty-Eighth AAAI Conference on Artificial Intelligence ( AAAI - 24 ) apeutic molecules , GxVAEs generate molecules that are biologically meaningful in the context of specific diseases using gene expression profiles .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 177, "end": 183}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Figure 1 shows an overview of the GxVAEs .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 34, "end": 40}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "GxVAEs mainly consists of two joint VAEs , namely ProfileVAE and MolVAE .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 0, "end": 6}, {"text": "VAEs", "start": 36, "end": 40}, {"text": "ProfileVAE", "start": 50, "end": 60}, {"text": "MolVAE", "start": 65, "end": 71}]}, "relations": {"part of": [{"head": {"text": "VAEs", "start": 36, "end": 40}, "tail": {"text": "GxVAEs", "start": 0, "end": 6}}, {"head": {"text": "MolVAE", "start": 65, "end": 71}, "tail": {"text": "GxVAEs", "start": 0, "end": 6}}, {"head": {"text": "ProfileVAE", "start": 50, "end": 60}, "tail": {"text": "GxVAEs", "start": 0, "end": 6}}], "subclass of": [{"head": {"text": "ProfileVAE", "start": 50, "end": 60}, "tail": {"text": "VAEs", "start": 36, "end": 40}}, {"head": {"text": "MolVAE", "start": 65, "end": 71}, "tail": {"text": "VAEs", "start": 36, "end": 40}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "ProfileVAE acts as a feature extractor to extract low-dimensional feature vectors from high-dimensional gene expression profiles .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 0, "end": 10}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MolVAE consists of bidirectional GRUs that are conditioned on the features of gene expression profiles using non-canonical SMILES ( i.e . , variant SMILES ( Li and Yamanishi 2023 ) ) strings to generate hitlike molecules .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 0, "end": 6}, {"text": "bidirectional GRUs", "start": 19, "end": 37}]}, "relations": {"part of": [{"head": {"text": "bidirectional GRUs", "start": 19, "end": 37}, "tail": {"text": "MolVAE", "start": 0, "end": 6}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Note that teacher forcing ( Yan et al . 2023 ) is used to stabilize the training and accelerate the convergence of the GxVAEs during the training phase of the MolVAE .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 119, "end": 125}, {"text": "MolVAE", "start": 159, "end": 165}]}, "relations": {"part of": [{"head": {"text": "MolVAE", "start": 159, "end": 165}, "tail": {"text": "GxVAEs", "start": 119, "end": 125}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "ProfileVAE .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 0, "end": 10}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The goal of Profil-eVAE is to learn the marginal likelihood of gene expression profiles during the following generative process : max EQUATION where E [ • ] is an expectation operation , C is the latent variable , θ and ϕ denote the parameters of the ProfileVAE encoder and ProfileVAE decoder , respectively , and p ϕ ( X | C ) and q θ ( C | X ) are the likelihood function and posterior distribution , respectively .", "output": {"entities": {"method": [{"text": "Profil-eVAE", "start": 12, "end": 23}, {"text": "ProfileVAE encoder", "start": 251, "end": 269}, {"text": "ProfileVAE decoder", "start": 274, "end": 292}, {"text": "likelihood function", "start": 354, "end": 373}, {"text": "posterior distribution", "start": 378, "end": 400}]}, "relations": {"part of": [{"head": {"text": "posterior distribution", "start": 378, "end": 400}, "tail": {"text": "ProfileVAE encoder", "start": 251, "end": 269}}, {"head": {"text": "likelihood function", "start": 354, "end": 373}, "tail": {"text": "ProfileVAE decoder", "start": 274, "end": 292}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The loss function of the ProfileVAE can be formulated as follows : L G ( θ , ϕ , X , C , β ) = E q θ ( C | X ) [ log p ϕ ( X | C ) ] ( 2 ) - β • D KL ( q θ ( C | X ) | | p ϕ ( C ) ) , where β denotes the weight of the KL divergence D KL ( Joyce 2011 ) .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 25, "end": 35}, {"text": "KL divergence", "start": 218, "end": 231}]}, "relations": {"part of": [{"head": {"text": "KL divergence", "start": 218, "end": 231}, "tail": {"text": "ProfileVAE", "start": 25, "end": 35}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MolVAE .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 0, "end": 6}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Then , EQUATION ) Note that the feature vectors C of the extracted gene expression profiles are used as the conditions for MolVAE .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 123, "end": 129}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MolVAE aims to maximize the lower bound of the true logmarginal likelihood as follows : EQUATION - D KL ( q ψ ( Z | S ) | | p φ ( Z ) ) .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 0, "end": 6}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Three types of gene expression profiles were used to validate the effectiveness of the proposed GxVAEs . • Chemically induced profiles were collected from the LINCS L1000 database ( Duan et al . 2014 ) .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 96, "end": 102}], "dataset": [{"text": "LINCS L1000", "start": 159, "end": 170}]}, "relations": {"evaluated with": [{"head": {"text": "GxVAEs", "start": 96, "end": 102}, "tail": {"text": "LINCS L1000", "start": 159, "end": 170}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , gene expression profiles of MCF7 cell line treated with 13 , 755 molecules were used . • Target perturbation profiles were obtained from the LINCS database .", "output": {"entities": {"dataset": [{"text": "LINCS", "start": 148, "end": 153}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For example , we analyzed RAC-α serine / threonine-protein kinase ( AKT1 ) , RACβ serine / threonine-protein kinase ( AKT2 ) , Aurora B kinase ( AURKB ) , cysteine synthase A ( CTSK ) , epidermal growth factor receptor ( EGFR ) , histone deacetylase 1 ( HDAC1 ) , mammalian target of rapamycin ( MTOR ) , phosphatidylinositol 3 - kinase catalytic subunit ( PIK3CA ) , decapentaplegic homolog 3 ( SMAD3 ) , and tumor protein p53 ( TP53 ) , which are therapeutic proteins for cancers . • Disease-specific profiles were collected from the crowd extracted expression of differential signatures ( CREEDS ) database ( Wang et al . 2016 ) .", "output": {"entities": {"dataset": [{"text": "crowd extracted expression of differential signatures", "start": 536, "end": 589}, {"text": "CREEDS", "start": 592, "end": 598}]}, "relations": {"synonym of": [{"head": {"text": "CREEDS", "start": 592, "end": 598}, "tail": {"text": "crowd extracted expression of differential signatures", "start": 536, "end": 589}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Validity refers to the ratio of chemically valid molecules generated , which can be verified in practice using the RDKit tool ( Landrum 2013 ) .", "output": {"entities": {"method": [{"text": "RDKit tool", "start": 115, "end": 125}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A quantitative estimate of drug-likeness ( QED ) quantifies the likelihood that a molecule is a drug ( Appendix D ) .", "output": {"entities": {"method": [{"text": "quantitative estimate of drug-likeness", "start": 2, "end": 40}, {"text": "QED", "start": 43, "end": 46}]}, "relations": {"synonym of": [{"head": {"text": "QED", "start": 43, "end": 46}, "tail": {"text": "quantitative estimate of drug-likeness", "start": 2, "end": 40}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To demonstrate that ProfileVAE has the ability to extract the biological features of gene expression profiles , we compared the distributions of the input gene expression profiles and the reconstructed profiles .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 20, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The distributions of the reconstructed profiles approximate those of the corresponding validation data , which indicates that ProfileVAE extracted the features of the gene expression profiles well , and proved the effectiveness of the proposed GxVAEs . to those in the validation set .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 126, "end": 136}, {"text": "GxVAEs", "start": 244, "end": 250}]}, "relations": {"part of": [{"head": {"text": "ProfileVAE", "start": 126, "end": 136}, "tail": {"text": "GxVAEs", "start": 244, "end": 250}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Furthermore , Table 1 lists the statistics for the validation set and molecules generated by the proposed GxVAEs .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 106, "end": 112}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The average , maximum , minimum lengths , and the average molecular weight of the generated SMILES strings were basically consistent with the SMILES strings in the original validation set , indicating that GxVAEs learned the data distributions of the SMILES strings well .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 206, "end": 212}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Table 2 shows the ability of the proposed Gx-VAEs to generate candidate molecules .", "output": {"entities": {"method": [{"text": "Gx-VAEs", "start": 42, "end": 49}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Note that Expres-sionGAN has limited ability to generate valid molecules , thus we only compared GxVAEs to the TRIOMPHE baseline .", "output": {"entities": {"method": [{"text": "Expres-sionGAN", "start": 10, "end": 24}, {"text": "GxVAEs", "start": 97, "end": 103}, {"text": "TRIOMPHE", "start": 111, "end": 119}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 97, "end": 103}, "tail": {"text": "TRIOMPHE", "start": 111, "end": 119}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The experimental results demonstrate that the validity of the proposed GxVAEs is three times higher than that of TRIOMPHE for generating ligand-like molecules using the gene expression profiles of the ten target proteins , reaching at least 78.0% ( HDAC1 ) .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 71, "end": 77}, {"text": "TRIOMPHE", "start": 113, "end": 121}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 71, "end": 77}, "tail": {"text": "TRIOMPHE", "start": 113, "end": 121}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Moreover , the uniqueness of the GxVAEs exceeds that of TRIOMPHE except for PIK3CA ( 93.5% < 97.2% ) .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 33, "end": 39}, {"text": "TRIOMPHE", "start": 56, "end": 64}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 33, "end": 39}, "tail": {"text": "TRIOMPHE", "start": 56, "end": 64}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , the novelty of the GxVAEs ( 97.7% ) is close to that of TRIOMPHE .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 34, "end": 40}, {"text": "TRIOMPHE", "start": 71, "end": 79}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 34, "end": 40}, "tail": {"text": "TRIOMPHE", "start": 71, "end": 79}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Overall , the proposed GxVAEs provided a sufficient number of candidate ligands for the ten target proteins .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 23, "end": 29}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Table 3 compares the Tanimoto coefficients of GxVAEs with those of the two SOTA models .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 46, "end": 52}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The experimental results illustrate that the Tanimoto coefficients of the candidate ligands generated by GxVAEs for the ten target proteins were much higher than those of the two SOTA models .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 105, "end": 111}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For example , the Tanimoto coefficient of GxVAEs for AKT1 was 2.7 and 2.0 times higher than those of the two baselines .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 42, "end": 48}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The chemical structures of the hit-like molecules generated by GxVAEs are all similar to the structures of the known ligands .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 63, "end": 69}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Overall , the proposed GxVAEs show excellent performance in generating hit-like molecules from the gene expression profiles , and the biological activity of the generated molecules far exceeded the SOTA baselines .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 23, "end": 29}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Figure 5 shows the process undertaken by GxVAEs for therapeutic molecular generation .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 41, "end": 47}], "task": [{"text": "therapeutic molecular generation", "start": 52, "end": 84}]}, "relations": {"used for": [{"head": {"text": "GxVAEs", "start": 41, "end": 47}, "tail": {"text": "therapeutic molecular generation", "start": 52, "end": 84}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Finally , the disease reversal profiles were input into GxVAEs to generate candidate therapeutic molecules .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 56, "end": 62}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We compared the results generated by GxVAEs with those generated by DRAGONET ( Yamanaka et al . 2023 ) .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 37, "end": 43}, {"text": "DRAGONET", "start": 68, "end": 76}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 37, "end": 43}, "tail": {"text": "DRAGONET", "start": 68, "end": 76}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For a fair comparison , we used the same datasets on patient gene expression profiles and molecule chemical structures in DRAGONET .", "output": {"entities": {"method": [{"text": "DRAGONET", "start": 122, "end": 130}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Figure 6 shows the therapeutic molecules generated by DRAGONET and GxVAEs , as well as the Tanimoto coefficients calculated relative to known approved drugs .", "output": {"entities": {"method": [{"text": "DRAGONET", "start": 54, "end": 62}, {"text": "GxVAEs", "start": 67, "end": 73}]}, "relations": {"compare with": [{"head": {"text": "DRAGONET", "start": 54, "end": 62}, "tail": {"text": "GxVAEs", "start": 67, "end": 73}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The Tanimoto coefficients between hydrocortisone and the molecules generated by GxVAEs using the disease reversal profile of patients having atopic dermatitis reached 1.0 .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 80, "end": 86}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This result indicates that GxVAEs effectively captured the structural features of the drugs approved for the treatment of atopic dermatitis .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 27, "end": 33}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Furthermore , the molecules generated by GxVAEs for treating gastric cancer and Alzheimer's disease showed structural features that were similar to those of known approved drugs .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 41, "end": 47}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Thus , the proposed GxVAEs can generate molecules that have higher therapeutic properties than DRAGONET .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 20, "end": 26}, {"text": "DRAGONET", "start": 95, "end": 103}]}, "relations": {"compare with": [{"head": {"text": "GxVAEs", "start": 20, "end": 26}, "tail": {"text": "DRAGONET", "start": 95, "end": 103}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We proposed GxVAEs , which consisted of two joint VAEs ( i.e . , ProfileVAE and MolVAE ) , to generate hit-like molecules from gene expression profiles .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 12, "end": 18}, {"text": "VAEs", "start": 50, "end": 54}, {"text": "ProfileVAE", "start": 65, "end": 75}, {"text": "MolVAE", "start": 80, "end": 86}]}, "relations": {"part of": [{"head": {"text": "VAEs", "start": 50, "end": 54}, "tail": {"text": "GxVAEs", "start": 12, "end": 18}}, {"head": {"text": "ProfileVAE", "start": 65, "end": 75}, "tail": {"text": "GxVAEs", "start": 12, "end": 18}}, {"head": {"text": "MolVAE", "start": 80, "end": 86}, "tail": {"text": "GxVAEs", "start": 12, "end": 18}}], "subclass of": [{"head": {"text": "ProfileVAE", "start": 65, "end": 75}, "tail": {"text": "VAEs", "start": 50, "end": 54}}, {"head": {"text": "MolVAE", "start": 80, "end": 86}, "tail": {"text": "VAEs", "start": 50, "end": 54}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "ProfileVAE extracted the features of the gene expression profiles , which were then used as conditions to guide MolVAE in producing hit-like molecules .", "output": {"entities": {"method": [{"text": "ProfileVAE", "start": 0, "end": 10}, {"text": "MolVAE", "start": 112, "end": 118}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The experimental results showed that GxVAEs outperformed the current SOTA baselines and efficiently generated hit-like molecules from gene expression profiles .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 37, "end": 43}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Furthermore , we showed the capability of GxVAEs to create molecular structures with the potential therapeutic effects for various diseases from patients ' disease profiles .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 42, "end": 48}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "One limitation of GxVAEs is that the diversity may be influenced by the size of the latent space .", "output": {"entities": {"method": [{"text": "GxVAEs", "start": 18, "end": 24}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "If an implementation of MolVAE adopts a fixed latent vector , it could potentially constrain the diversity of newly generated molecules .", "output": {"entities": {"method": [{"text": "MolVAE", "start": 24, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "XLUMINA : An Auto-differentiating Discovery Framework for Super-Resolution Microscopy In this work we introduce XLUMINA , an original computational framework designed for the discovery of novel optical hardware in super-resolution microscopy .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 0, "end": 7}, {"text": "Auto-differentiating Discovery Framework", "start": 13, "end": 53}, {"text": "XLUMINA", "start": 112, "end": 119}], "task": [{"text": "Super-Resolution Microscopy", "start": 58, "end": 85}, {"text": "super-resolution microscopy", "start": 214, "end": 241}]}, "relations": {"subclass of": [{"head": {"text": "XLUMINA", "start": 0, "end": 7}, "tail": {"text": "Auto-differentiating Discovery Framework", "start": 13, "end": 53}}], "used for": [{"head": {"text": "Auto-differentiating Discovery Framework", "start": 13, "end": 53}, "tail": {"text": "Super-Resolution Microscopy", "start": 58, "end": 85}}, {"head": {"text": "XLUMINA", "start": 0, "end": 7}, "tail": {"text": "Super-Resolution Microscopy", "start": 58, "end": 85}}, {"head": {"text": "XLUMINA", "start": 112, "end": 119}, "tail": {"text": "super-resolution microscopy", "start": 214, "end": 241}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our framework offers auto-differentiation capabilities , allowing for the fast and efficient simulation and automated design of entirely new optical setups from scratch .", "output": {"entities": {"method": [{"text": "auto-differentiation", "start": 21, "end": 41}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We showcase its potential by rediscovering three foundational experiments , each one covering different areas in optics : an optical telescope , STED microscopy and the focusing beyond the diffraction limit of a radially polarized light beam .", "output": {"entities": {"method": [{"text": "STED", "start": 145, "end": 149}, {"text": "polarized light beam", "start": 221, "end": 241}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "With XLUMINA , can we go beyond simple optimization and calibration of known experimental setups , opening the door to potentially uncovering new microscopy concepts within the vast landscape of experimental possibilities .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 5, "end": 12}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "FINAL SET-UP AI-based exploration tool Objective function e.g . spot size , ϕ = π FWHMxFWHMy 4 Enormously large search space OPTICS SIMULATOR FINAL SET-UP The space of all possible experimental optical configurations is enormous .", "output": {"entities": {"method": [{"text": "AI-based exploration tool", "start": 13, "end": 38}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This is where AI-based exploration techniques could provide enormous benefit , by exploring the space in a fast , unbiased way [ 1 , 2 ] .", "output": {"entities": {"method": [{"text": "AI-based exploration techniques", "start": 14, "end": 45}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Among them , the discovery of super-resolution ( SR ) methods , which circumvent the classical diffraction limit of light , stand out in particular .", "output": {"entities": {"method": [{"text": "super-resolution", "start": 30, "end": 46}, {"text": "SR", "start": 49, "end": 51}]}, "relations": {"synonym of": [{"head": {"text": "SR", "start": 49, "end": 51}, "tail": {"text": "super-resolution", "start": 30, "end": 46}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Examples for versatile and powerful SR techniques are STED [ 10 ] , PALM / F-PALM [ 11 , 12 ] , ( d ) STORM [ 13 , 14 ] , SIM [ 15 ] , and MINFLUX [ 16 ] , with considerable impact in biology [ 17 ] [ 18 ] [ 19 ] , chemistry [ 20 ] and material sciences [ 21 ] for example .", "output": {"entities": {"method": [{"text": "SR", "start": 36, "end": 38}, {"text": "STED", "start": 54, "end": 58}, {"text": "PALM / F-PALM", "start": 68, "end": 81}, {"text": "STORM", "start": 102, "end": 107}, {"text": "SIM", "start": 122, "end": 125}, {"text": "MINFLUX", "start": 139, "end": 146}]}, "relations": {"subclass of": [{"head": {"text": "STED", "start": 54, "end": 58}, "tail": {"text": "SR", "start": 36, "end": 38}}, {"head": {"text": "PALM / F-PALM", "start": 68, "end": 81}, "tail": {"text": "SR", "start": 36, "end": 38}}, {"head": {"text": "STORM", "start": 102, "end": 107}, "tail": {"text": "SR", "start": 36, "end": 38}}, {"head": {"text": "SIM", "start": 122, "end": 125}, "tail": {"text": "SR", "start": 36, "end": 38}}, {"head": {"text": "MINFLUX", "start": 139, "end": 146}, "tail": {"text": "SR", "start": 36, "end": 38}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Rather , this work sets out to discover novel , experimentally viable concepts for advanced optical microscopy that are at-present entirely untapped .", "output": {"entities": {"task": [{"text": "optical microscopy", "start": 92, "end": 110}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The simulator can either be called directly by gradient-based optimization techniques , or it can be used for generating the training data for deep-learning-based surrogate models .", "output": {"entities": {"method": [{"text": "gradient-based optimization techniques", "start": 47, "end": 85}, {"text": "deep-learning-based surrogate models", "start": 143, "end": 179}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We introduce XLUMINA , an efficient framework with auto-differentiation capabilities [ 23 ] for the ultimate goal of discovering new optical design principles .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 13, "end": 20}, {"text": "auto-differentiation", "start": 51, "end": 71}]}, "relations": {"part of": [{"head": {"text": "auto-differentiation", "start": 51, "end": 71}, "tail": {"text": "XLUMINA", "start": 13, "end": 20}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We demonstrate our approach on three foundational optical layouts : a telescope version , the polarization-based beam shaping as used in STED ( stimulated emission depletion ) microscopy [ 10 ] and the sharp focus of a radially polarized light beam [ 24 ] .", "output": {"entities": {"method": [{"text": "polarization-based beam shaping", "start": 94, "end": 125}, {"text": "STED", "start": 137, "end": 141}, {"text": "stimulated emission depletion", "start": 144, "end": 173}, {"text": "radially polarized light beam", "start": 219, "end": 248}]}, "relations": {"part of": [{"head": {"text": "polarization-based beam shaping", "start": 94, "end": 125}, "tail": {"text": "STED", "start": 137, "end": 141}}], "synonym of": [{"head": {"text": "stimulated emission depletion", "start": 144, "end": 173}, "tail": {"text": "STED", "start": 137, "end": 141}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Rather , the future application of XLUMINA is the AI-driven discovery of completely novel physical concepts for advanced optical microscopy .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 35, "end": 42}], "task": [{"text": "optical microscopy", "start": 121, "end": 139}]}, "relations": {"used for": [{"head": {"text": "XLUMINA", "start": 35, "end": 42}, "tail": {"text": "optical microscopy", "start": 121, "end": 139}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Optimization in microscopy Our approach is radically different from previous strategies that employ AI for data-driven design of single optical elements [ 25 , 26 ] or data analysis in microscopy , e.g . denoising , contrast enhancement or point-spread-function ( PSF ) engineering [ 27 ] .", "output": {"entities": {"method": [{"text": "denoising", "start": 204, "end": 213}, {"text": "contrast enhancement", "start": 216, "end": 236}, {"text": "point-spread-function", "start": 240, "end": 261}, {"text": "PSF", "start": 264, "end": 267}]}, "relations": {"synonym of": [{"head": {"text": "PSF", "start": 264, "end": 267}, "tail": {"text": "point-spread-function", "start": 240, "end": 261}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In contrast , XLUMINA is equipped with tools for simulate , optimize and automatically design new optical setups and concepts from scratch .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 14, "end": 21}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The main approach is the development of efficient PDE-solvers for Maxwell's equations , including efficient ways to compute the gradients of the vast amount of parameters , usually by a physics-inspired technique called the adjoint method [ 38 , 39 ] .", "output": {"entities": {"method": [{"text": "PDE-solvers", "start": 50, "end": 61}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Interestingly , the adjoint method can be seen as a special case of auto-differentiation ( which we use ) [ 39 ] .", "output": {"entities": {"method": [{"text": "auto-differentiation", "start": 68, "end": 88}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Several open-source software tools , like Diffractio for light diffraction and interference simulations [ 41 ] , Finesse for simulating gravitational wave detectors [ 42 ] , and POPPY , developed as a part of the simulation package of the James Webb Telescope [ 43 ] , facilitate classical optics phenomena simulations .", "output": {"entities": {"method": [{"text": "Diffractio", "start": 42, "end": 52}, {"text": "Finesse", "start": 113, "end": 120}, {"text": "POPPY", "start": 178, "end": 183}], "task": [{"text": "light diffraction", "start": 57, "end": 74}, {"text": "interference simulations", "start": 79, "end": 103}, {"text": "simulating gravitational wave detectors", "start": 125, "end": 164}, {"text": "optics phenomena simulations", "start": 290, "end": 318}]}, "relations": {"used for": [{"head": {"text": "Diffractio", "start": 42, "end": 52}, "tail": {"text": "light diffraction", "start": 57, "end": 74}}, {"head": {"text": "Diffractio", "start": 42, "end": 52}, "tail": {"text": "interference simulations", "start": 79, "end": 103}}, {"head": {"text": "Finesse", "start": 113, "end": 120}, "tail": {"text": "simulating gravitational wave detectors", "start": 125, "end": 164}}, {"head": {"text": "POPPY", "start": 178, "end": 183}, "tail": {"text": "optics phenomena simulations", "start": 290, "end": 318}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "There are also specialized resources like those focusing on the design of Laguerre-Gaussian mode sorters utilizing multi-plane light conversion ( MPLC ) methods [ 44 ] .", "output": {"entities": {"method": [{"text": "Laguerre-Gaussian", "start": 74, "end": 91}, {"text": "multi-plane light conversion", "start": 115, "end": 143}, {"text": "MPLC", "start": 146, "end": 150}]}, "relations": {"part of": [{"head": {"text": "multi-plane light conversion", "start": 115, "end": 143}, "tail": {"text": "Laguerre-Gaussian", "start": 74, "end": 91}}], "synonym of": [{"head": {"text": "MPLC", "start": 146, "end": 150}, "tail": {"text": "multi-plane light conversion", "start": 115, "end": 143}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "While these software offer optics simulation capabilities , XLUMINA uniquely integrates simulation with AI-driven automated design powered with JAX's autodiff and just-in-time ( jit ) compilation capabilities . 2 Software workflow and performance XLUMINA allows for the simulation of classical optics hardware configurations and enables the optimization and automated discovery of new setup designs .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 60, "end": 67}, {"text": "AI-driven automated design", "start": 104, "end": 130}, {"text": "XLUMINA", "start": 247, "end": 254}]}, "relations": {"part of": [{"head": {"text": "AI-driven automated design", "start": 104, "end": 130}, "tail": {"text": "XLUMINA", "start": 60, "end": 67}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The software is developed using JAX [ 45 ] , which provides an advantage of heightened computational speed while seamlessly integrating the auto-differentiation framework [ 23 ] .", "output": {"entities": {"method": [{"text": "auto-differentiation", "start": 140, "end": 160}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The first benchmark is to rediscover highly impactful microscopy strategies , such as STED microscopy [ 10 ] or the sharp focus of a radially polarized light beam [ 24 ] , as each of these incorporate different ideas or physical properties of light .", "output": {"entities": {"method": [{"text": "microscopy strategies", "start": 54, "end": 75}, {"text": "STED", "start": 86, "end": 90}, {"text": "polarized light beam", "start": 142, "end": 162}]}, "relations": {"subclass of": [{"head": {"text": "STED", "start": 86, "end": 90}, "tail": {"text": "microscopy strategies", "start": 54, "end": 75}}, {"head": {"text": "polarized light beam", "start": 142, "end": 162}, "tail": {"text": "microscopy strategies", "start": 54, "end": 75}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To that end , the algorithm is equipped with an optics simulator , which contains a diverse set of optical manipulation , interaction , and measurement technologies .", "output": {"entities": {"method": [{"text": "optics simulator", "start": 48, "end": 64}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The simulator enables , among many other features , to define light sources ( of any wavelength and power ) , phase masks ( i.e . , spatial light modulators , SLMs ) , polarizers , variable retarders ( e.g . , liquid crystal displays , LCDs ) , diffraction gratings , and high NA lenses to replicate strong focusing conditions .", "output": {"entities": {"method": [{"text": "phase masks", "start": 110, "end": 121}, {"text": "spatial light modulators", "start": 132, "end": 156}, {"text": "SLMs", "start": 159, "end": 163}, {"text": "polarizers", "start": 168, "end": 178}, {"text": "variable retarders", "start": 181, "end": 199}, {"text": "liquid crystal displays", "start": 210, "end": 233}, {"text": "LCDs", "start": 236, "end": 240}, {"text": "diffraction gratings", "start": 245, "end": 265}, {"text": "NA lenses", "start": 277, "end": 286}]}, "relations": {"subclass of": [{"head": {"text": "spatial light modulators", "start": 132, "end": 156}, "tail": {"text": "phase masks", "start": 110, "end": 121}}, {"head": {"text": "liquid crystal displays", "start": 210, "end": 233}, "tail": {"text": "variable retarders", "start": 181, "end": 199}}], "synonym of": [{"head": {"text": "SLMs", "start": 159, "end": 163}, "tail": {"text": "spatial light modulators", "start": 132, "end": 156}}, {"head": {"text": "LCDs", "start": 236, "end": 240}, "tail": {"text": "liquid crystal displays", "start": 210, "end": 233}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Light propagation and diffraction is simulated by two methods , each available for both scalar and vectorial regimes : the fast-Fourier-transform ( FFT ) based numerical integration of the Rayleigh-Sommerfeld ( RS ) diffraction integral [ 46 , 47 ] and the Chirped z-transform ( CZT ) [ 48 ] .", "output": {"entities": {"method": [{"text": "fast-Fourier-transform", "start": 123, "end": 145}, {"text": "FFT", "start": 148, "end": 151}, {"text": "Rayleigh-Sommerfeld", "start": 189, "end": 208}, {"text": "RS", "start": 211, "end": 213}, {"text": "Chirped z-transform", "start": 257, "end": 276}, {"text": "CZT", "start": 279, "end": 282}]}, "relations": {"synonym of": [{"head": {"text": "FFT", "start": 148, "end": 151}, "tail": {"text": "fast-Fourier-transform", "start": 123, "end": 145}}, {"head": {"text": "RS", "start": 211, "end": 213}, "tail": {"text": "Rayleigh-Sommerfeld", "start": 189, "end": 208}}, {"head": {"text": "CZT", "start": 279, "end": 282}, "tail": {"text": "Chirped z-transform", "start": 257, "end": 276}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The CZT is an accelerated version of the RS algorithm , which allows for arbitrary selection and sampling of the region of interest .", "output": {"entities": {"method": [{"text": "CZT", "start": 4, "end": 7}, {"text": "RS", "start": 41, "end": 43}]}, "relations": {"subclass of": [{"head": {"text": "CZT", "start": 4, "end": 7}, "tail": {"text": "RS", "start": 41, "end": 43}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Some functionalities of XLUMINA's optics simulator ( e.g . , optical propagation algorithms , planar lens or amplitude masks ) are inspired in an open-source NumPy-based Python module for diffraction and interferometry simulation , Diffractio [ 41 ] , although we have rewritten and modified these approaches to combine them with JAX's just-in-time ( jit ) functionality .", "output": {"entities": {"method": [{"text": "XLUMINA's optics simulator", "start": 24, "end": 50}, {"text": "optical propagation algorithms", "start": 61, "end": 91}, {"text": "planar lens", "start": 94, "end": 105}, {"text": "amplitude masks", "start": 109, "end": 124}, {"text": "Diffractio", "start": 232, "end": 242}], "task": [{"text": "diffraction", "start": 188, "end": 199}, {"text": "interferometry simulation", "start": 204, "end": 229}]}, "relations": {"subclass of": [{"head": {"text": "optical propagation algorithms", "start": 61, "end": 91}, "tail": {"text": "XLUMINA's optics simulator", "start": 24, "end": 50}}, {"head": {"text": "planar lens", "start": 94, "end": 105}, "tail": {"text": "XLUMINA's optics simulator", "start": 24, "end": 50}}, {"head": {"text": "amplitude masks", "start": 109, "end": 124}, "tail": {"text": "XLUMINA's optics simulator", "start": 24, "end": 50}}], "used for": [{"head": {"text": "XLUMINA's optics simulator", "start": 24, "end": 50}, "tail": {"text": "diffraction", "start": 188, "end": 199}}, {"head": {"text": "amplitude masks", "start": 109, "end": 124}, "tail": {"text": "diffraction", "start": 188, "end": 199}}, {"head": {"text": "planar lens", "start": 94, "end": 105}, "tail": {"text": "diffraction", "start": 188, "end": 199}}, {"head": {"text": "optical propagation algorithms", "start": 61, "end": 91}, "tail": {"text": "diffraction", "start": 188, "end": 199}}, {"head": {"text": "XLUMINA's optics simulator", "start": 24, "end": 50}, "tail": {"text": "interferometry simulation", "start": 204, "end": 229}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "On top of that , we developed completely new functions ( e.g . , LCDs or propagation through high NA objective lens with CZT methods , to name a few ) which significantly expand the software capabilities .", "output": {"entities": {"method": [{"text": "LCDs", "start": 65, "end": 69}, {"text": "NA objective lens", "start": 98, "end": 115}, {"text": "CZT", "start": 121, "end": 124}]}, "relations": {"part of": [{"head": {"text": "CZT", "start": 121, "end": 124}, "tail": {"text": "NA objective lens", "start": 98, "end": 115}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The most important hardware addition on the optical simulator are the SLMs , each pixel of which possesses an independent ( and variable ) phase value .", "output": {"entities": {"method": [{"text": "optical simulator", "start": 44, "end": 61}, {"text": "SLMs", "start": 70, "end": 74}]}, "relations": {"part of": [{"head": {"text": "SLMs", "start": 70, "end": 74}, "tail": {"text": "optical simulator", "start": 44, "end": 61}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "They serve as a universal approximation for phase masks ( including lenses ) and offer a computational advantage : given a specific pixel resolution , they allow for unrestricted phase design selection .", "output": {"entities": {"method": [{"text": "phase masks", "start": 44, "end": 55}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In addition , we defined under the name of super-SLM ( sSLM ) a hardware-box-type which consists of two SLMs , each one independently imprinting a phase mask on the horizontal and vertical orthogonal polarization components of the field .", "output": {"entities": {"method": [{"text": "super-SLM", "start": 43, "end": 52}, {"text": "sSLM", "start": 55, "end": 59}, {"text": "SLMs", "start": 104, "end": 108}, {"text": "phase mask", "start": 147, "end": 157}]}, "relations": {"synonym of": [{"head": {"text": "sSLM", "start": 55, "end": 59}, "tail": {"text": "super-SLM", "start": 43, "end": 52}}], "part of": [{"head": {"text": "SLMs", "start": 104, "end": 108}, "tail": {"text": "super-SLM", "start": 43, "end": 52}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To include the automated discovery feature , XLUMINA's optical simulator and optimizer are tied together by the loss function .", "output": {"entities": {"method": [{"text": "XLUMINA's optical simulator", "start": 45, "end": 72}, {"text": "optimizer", "start": 77, "end": 86}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Thus , it is essential to reduce the computation time by maximizing the speed of optical simulation functions .", "output": {"entities": {"method": [{"text": "optical simulation functions", "start": 81, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Thus , we evaluate the performance of our optimized functions against their counterparts in Diffractio by propagating a Gaussian beam within a computational window sized at 2048 × 2048 .", "output": {"entities": {"method": [{"text": "Diffractio", "start": 92, "end": 102}, {"text": "Gaussian beam", "start": 120, "end": 133}]}, "relations": {"part of": [{"head": {"text": "Gaussian beam", "start": 120, "end": 133}, "tail": {"text": "Diffractio", "start": 92, "end": 102}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The average run-time for both Diffractio and our approach is shown in Figure 2a .", "output": {"entities": {"method": [{"text": "Diffractio", "start": 30, "end": 40}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Generally , our methods significantly enhance computational speeds for simulating light diffraction and propagation .", "output": {"entities": {"task": [{"text": "light diffraction", "start": 82, "end": 99}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For instance , we observe a speedup of roughly a factor of 2 for RS and VCZT and about 2.5 for VRS using the CPU .", "output": {"entities": {"method": [{"text": "RS", "start": 65, "end": 67}, {"text": "VCZT", "start": 72, "end": 76}, {"text": "VRS", "start": 95, "end": 98}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "CZT has less significant speedup , but there is still a 0.5 - second improvement .", "output": {"entities": {"method": [{"text": "CZT", "start": 0, "end": 3}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "When it comes to the nature of the optimizer , it can be either direct ( gradient-based ) or deep learningbased ( surrogate models or deep generative models , e.g . , variational autoencoders [ 49 ] ) .", "output": {"entities": {"method": [{"text": "gradient-based", "start": 73, "end": 87}, {"text": "deep learningbased", "start": 93, "end": 111}, {"text": "surrogate models", "start": 114, "end": 130}, {"text": "deep generative models", "start": 134, "end": 156}, {"text": "variational autoencoders", "start": 167, "end": 191}]}, "relations": {"subclass of": [{"head": {"text": "surrogate models", "start": 114, "end": 130}, "tail": {"text": "deep learningbased", "start": 93, "end": 111}}, {"head": {"text": "deep generative models", "start": 134, "end": 156}, "tail": {"text": "deep learningbased", "start": 93, "end": 111}}, {"head": {"text": "variational autoencoders", "start": 167, "end": 191}, "tail": {"text": "deep generative models", "start": 134, "end": 156}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this work , we adopt a gradient-based strategy , where the experimental setup's parameters are adjusted iteratively in the steepest descent direction .", "output": {"entities": {"method": [{"text": "gradient-based strategy", "start": 26, "end": 49}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To chose the optimizer , we evaluate the convergence time of two gradient-descent techniques : the Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) algorithm , which numerically computes gradients and higher-order derivative approximations , and the adaptive moment estimation ( ADAM ) , an instance of the stochastic-gradient-descent ( SGD ) method .", "output": {"entities": {"method": [{"text": "gradient-descent techniques", "start": 65, "end": 92}, {"text": "Broyden-Fletcher-Goldfarb-Shanno", "start": 99, "end": 131}, {"text": "BFGS", "start": 134, "end": 138}, {"text": "adaptive moment estimation", "start": 243, "end": 269}, {"text": "ADAM", "start": 272, "end": 276}, {"text": "stochastic-gradient-descent", "start": 300, "end": 327}, {"text": "SGD", "start": 330, "end": 333}]}, "relations": {"subclass of": [{"head": {"text": "Broyden-Fletcher-Goldfarb-Shanno", "start": 99, "end": 131}, "tail": {"text": "gradient-descent techniques", "start": 65, "end": 92}}, {"head": {"text": "adaptive moment estimation", "start": 243, "end": 269}, "tail": {"text": "gradient-descent techniques", "start": 65, "end": 92}}, {"head": {"text": "adaptive moment estimation", "start": 243, "end": 269}, "tail": {"text": "stochastic-gradient-descent", "start": 300, "end": 327}}], "synonym of": [{"head": {"text": "BFGS", "start": 134, "end": 138}, "tail": {"text": "Broyden-Fletcher-Goldfarb-Shanno", "start": 99, "end": 131}}, {"head": {"text": "ADAM", "start": 272, "end": 276}, "tail": {"text": "adaptive moment estimation", "start": 243, "end": 269}}, {"head": {"text": "SGD", "start": 330, "end": 333}, "tail": {"text": "stochastic-gradient-descent", "start": 300, "end": 327}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "While BFGS is part of the open-source SciPy Python library and operates on the CPU , ADAM is integrated within the JAX library and runs in both CPU and GPU .", "output": {"entities": {"method": [{"text": "BFGS", "start": 6, "end": 10}, {"text": "ADAM", "start": 85, "end": 89}]}, "relations": {"compare with": [{"head": {"text": "BFGS", "start": 6, "end": 10}, "tail": {"text": "ADAM", "start": 85, "end": 89}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For the evaluation , we simulate a Gaussian beam interacting with a phase mask .", "output": {"entities": {"method": [{"text": "Gaussian beam", "start": 35, "end": 48}, {"text": "phase mask", "start": 68, "end": 78}]}, "relations": {"part of": [{"head": {"text": "phase mask", "start": 68, "end": 78}, "tail": {"text": "Gaussian beam", "start": 35, "end": 48}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The objective function is the mean squared error between the detected light and the ground truth , characterized by a Gaussian beam with a spiral phase imprinted on its wavefront .", "output": {"entities": {"method": [{"text": "mean squared error", "start": 30, "end": 48}, {"text": "Gaussian beam", "start": 118, "end": 131}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Initializing with an arbitrary phase mask configuration , we run both BFGS and ADAM optimizers over different computational windows and devices , as depicted in Fig . 2b .", "output": {"entities": {"method": [{"text": "phase mask", "start": 31, "end": 41}, {"text": "BFGS", "start": 70, "end": 74}, {"text": "ADAM", "start": 79, "end": 83}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "On the CPU , BFGS exhibits exponential scaling in convergence time , reaching about 6500 seconds for 250 × 250 pixel window .", "output": {"entities": {"method": [{"text": "BFGS", "start": 13, "end": 17}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In contrast , ADAM demonstrates superior efficiency , reducing it to roughly 2600 seconds .", "output": {"entities": {"method": [{"text": "ADAM", "start": 14, "end": 18}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This makes the GPU-accelerated ADAM approach more appropriate for efficient experimentation .", "output": {"entities": {"method": [{"text": "ADAM", "start": 31, "end": 35}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Overall , the computational performance of XLUMINA highlights its suitability for running complex simulations and optimizations with a high level of efficiency .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 43, "end": 50}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this section , we showcase the virtual optical designs generated by XLUMINA .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 71, "end": 78}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This arrangement performs optical Fourier transformations of input light with magnifications determined by the ratio f 2 / f 1 .", "output": {"entities": {"method": [{"text": "optical Fourier transformations", "start": 26, "end": 57}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To revisit this design with a magnification of 2x , we encoded the virtual setup depicted in Fig . 3a , in which traditional lenses are replaced by spatial light modulators ( SLMs ) .", "output": {"entities": {"method": [{"text": "spatial light modulators", "start": 148, "end": 172}, {"text": "SLMs", "start": 175, "end": 179}]}, "relations": {"subclass of": [{"head": {"text": "SLMs", "start": 175, "end": 179}, "tail": {"text": "spatial light modulators", "start": 148, "end": 172}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The parameter space includes the distances , z 1 , z 2 and z 3 ( measured in millimeters ) and the phase masks ( measured in radians ) of the two SLMs with a resolution of 1024 × 1024 pixels .", "output": {"entities": {"method": [{"text": "phase masks", "start": 99, "end": 110}, {"text": "SLMs", "start": 146, "end": 150}]}, "relations": {"part of": [{"head": {"text": "phase masks", "start": 99, "end": 110}, "tail": {"text": "SLMs", "start": 146, "end": 150}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Each sample consists of a Gaussian beam shaped by amplitude masks in various forms ( circles , rectangles , squares and rings ) , with varying sizes and orientations .", "output": {"entities": {"method": [{"text": "Gaussian beam", "start": 26, "end": 39}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The cost function is the mean squared error between the dataset's output and the detected intensity pattern from the virtual setup .", "output": {"entities": {"method": [{"text": "mean squared error", "start": 25, "end": 43}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The solution depicts lens-like quadratic phases in both SLMs .", "output": {"entities": {"method": [{"text": "lens-like quadratic", "start": 21, "end": 40}, {"text": "SLMs", "start": 56, "end": 60}]}, "relations": {"part of": [{"head": {"text": "lens-like quadratic", "start": 21, "end": 40}, "tail": {"text": "SLMs", "start": 56, "end": 60}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This suggests that phase mask of SLM #1 might be compensating for this deviation .", "output": {"entities": {"method": [{"text": "phase mask", "start": 19, "end": 29}, {"text": "SLM", "start": 33, "end": 36}]}, "relations": {"part of": [{"head": {"text": "phase mask", "start": 19, "end": 29}, "tail": {"text": "SLM", "start": 33, "end": 36}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "On the other hand , we believe that the more precise solution for SLM #2 highlights its critical role in imaging .", "output": {"entities": {"method": [{"text": "SLM", "start": 66, "end": 69}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "STED microscopy STED microscopy [ 10 ] is based on excitation and spatially targeted depletion of fluorophores .", "output": {"entities": {"method": [{"text": "STED", "start": 0, "end": 4}, {"text": "STED", "start": 16, "end": 20}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In order to achieve this , a Gaussian-shaped excitation beam and a doughnut-shaped depletion beam are concentrically overlapped .", "output": {"entities": {"method": [{"text": "Gaussian-shaped excitation beam", "start": 29, "end": 60}, {"text": "doughnut-shaped depletion beam", "start": 67, "end": 97}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This effectively reduces the area of normal fluorescence , which leads to super-resolution imaging .", "output": {"entities": {"task": [{"text": "super-resolution imaging", "start": 74, "end": 98}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In order to generate a doughnut-shaped beam a spiral phase is imprinted into the wavefront of a Gaussian beam .", "output": {"entities": {"method": [{"text": "doughnut-shaped beam", "start": 23, "end": 43}, {"text": "Gaussian beam", "start": 96, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To revisit this principle , we virtually construct a simplified version of a STED-type setup as depicted in Fig . 4a .", "output": {"entities": {"method": [{"text": "STED-type", "start": 77, "end": 86}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It consists of two light sources generating Gaussian beams corresponding to the depletion and excitation beams with wavelengths of 650 nm and 532 nm , respectively .", "output": {"entities": {"method": [{"text": "Gaussian beams", "start": 44, "end": 58}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Within the depletion beam's optical path , we place an SLM of 2048 × 2048 resolution and a computational pixel size of 1.95µm .", "output": {"entities": {"method": [{"text": "SLM", "start": 55, "end": 58}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this instance , the parameter space is defined by the SLM .", "output": {"entities": {"method": [{"text": "SLM", "start": 57, "end": 60}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For this particular instance , the detected intensity corresponds to the radial component , In Fig . 4b , we present the STED spiral phase mask [ 10 ] and the identified solution for ε = 0.7 .", "output": {"entities": {"method": [{"text": "STED", "start": 121, "end": 125}, {"text": "phase mask", "start": 133, "end": 143}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "From a random initial phase mask in the SLM , the system converged into a pattern alike to the spiral phase .", "output": {"entities": {"method": [{"text": "random initial phase mask", "start": 7, "end": 32}, {"text": "SLM", "start": 40, "end": 43}]}, "relations": {"part of": [{"head": {"text": "random initial phase mask", "start": 7, "end": 32}, "tail": {"text": "SLM", "start": 40, "end": 43}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Other solutions presented noisy phase patterns which failed to achieve the essential doughnut-shaped depletion beam .", "output": {"entities": {"method": [{"text": "doughnut-shaped depletion beam", "start": 85, "end": 115}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Real-world STED setups demand almost perfect phase patterns ; even the minor misalignment can compromise the super-resolution STED phenomena .", "output": {"entities": {"method": [{"text": "Real-world STED", "start": 0, "end": 15}, {"text": "super-resolution STED", "start": 109, "end": 130}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "| E x | 2 + | E y | 2 , Sharper focus for a radially polarized light beam The final benchmark focuses on the generation of an ultra-sharp focus for a radially polarized beam , a feature that breaks the diffraction limit in the longitudinal direction as demonstrated by R .", "output": {"entities": {"method": [{"text": "polarized light beam", "start": 53, "end": 73}, {"text": "polarized beam", "start": 159, "end": 173}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The light source emits a 635 nm wavelength Gaussian beam that is linearly polarized .", "output": {"entities": {"method": [{"text": "Gaussian beam", "start": 43, "end": 56}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The original optical elements are replaced by an sSLM , each component of which has a resolution of 2048 × 2048 pixels and a computational pixel size of 1.46µm .", "output": {"entities": {"method": [{"text": "sSLM", "start": 49, "end": 53}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , we place an LCD with variable phase retardance η and orientation angle θ .", "output": {"entities": {"method": [{"text": "LCD", "start": 27, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The beam then passes through a high NA objective lens before reaching the detector screen .", "output": {"entities": {"method": [{"text": "NA objective lens", "start": 36, "end": 53}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Relevant data on the sSLM's phase masks , optical parameters , and the simulated spot size are showcased in Fig . 5b and Table 1 .", "output": {"entities": {"method": [{"text": "sSLM's", "start": 21, "end": 27}, {"text": "phase masks", "start": 28, "end": 39}]}, "relations": {"part of": [{"head": {"text": "phase masks", "start": 28, "end": 39}, "tail": {"text": "sSLM's", "start": 21, "end": 27}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "With regards to Solution #2 , the SLM phase pattern also shows a tilted forked grating of topological charge p = 1 .", "output": {"entities": {"method": [{"text": "SLM", "start": 34, "end": 37}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For comparison , we also feature the radial intensity profile of the diffraction-limited linearly polarized beam ( dotted orange line in Fig . 5e ) .", "output": {"entities": {"method": [{"text": "diffraction-limited linearly polarized beam", "start": 69, "end": 112}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This was crucial for our purpose to demonstrate how XLUMINA can compute and efficiently rediscover known techniques in advanced microscopy .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 52, "end": 59}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We aim to use XLUMINA to discover new microscopy concepts .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 14, "end": 21}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "From here , XLUMINA should be able to extract much more complex solutions which humans might not have thought about yet [ 2 ] .", "output": {"entities": {"method": [{"text": "XLUMINA", "start": 12, "end": 19}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this work , we present an efficient and reliable simulator for advanced optical microscopy .", "output": {"entities": {"method": [{"text": "simulator", "start": 52, "end": 61}], "task": [{"text": "optical microscopy", "start": 75, "end": 93}]}, "relations": {"used for": [{"head": {"text": "simulator", "start": 52, "end": 61}, "tail": {"text": "optical microscopy", "start": 75, "end": 93}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The simulator is developed in a modular way , and we plan to significantly expand it by adding more physical properties and features exploited in microscopy , for example , detailed coverage of frequency and time information , which might enable systems such as iSCAT [ 56 ] , structured illumination microscopy [ 57 ] , and localization microscopy [ 58 ] .", "output": {"entities": {"method": [{"text": "iSCAT", "start": 262, "end": 267}, {"text": "structured illumination microscopy", "start": 277, "end": 311}, {"text": "localization microscopy", "start": 325, "end": 348}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Looking further into the future , one can expect that matter-wave beams ( governed by Schrödinger's equation , which is closely related to the paraxial wave equation , a special case of the electromagnetic field ) can be simulated in the same framework .", "output": {"entities": {"method": [{"text": "matter-wave beams", "start": 54, "end": 71}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This might allow for the AI-based design of hybrid microscopy techniques using light and complex electron-beams [ 61 ] or coherent beams of high-mass particles [ 62 ] .", "output": {"entities": {"method": [{"text": "hybrid microscopy techniques", "start": 44, "end": 72}, {"text": "complex electron-beams", "start": 89, "end": 111}, {"text": "coherent beams of high-mass particles", "start": 122, "end": 159}]}, "relations": {"part of": [{"head": {"text": "complex electron-beams", "start": 89, "end": 111}, "tail": {"text": "hybrid microscopy techniques", "start": 44, "end": 72}}, {"head": {"text": "coherent beams of high-mass particles", "start": 122, "end": 159}, "tail": {"text": "hybrid microscopy techniques", "start": 44, "end": 72}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Label Words are Anchors : An Information Flow Perspective for Understanding In-Context Learning In-context learning ( ICL ) emerges as a promising capability of large language models ( LLMs ) by providing them with demonstration examples to perform diverse tasks .", "output": {"entities": {"method": [{"text": "In-Context Learning", "start": 76, "end": 95}, {"text": "In-context learning", "start": 96, "end": 115}, {"text": "ICL", "start": 118, "end": 121}, {"text": "large language models", "start": 161, "end": 182}, {"text": "LLMs", "start": 185, "end": 189}]}, "relations": {"synonym of": [{"head": {"text": "ICL", "start": 118, "end": 121}, "tail": {"text": "In-context learning", "start": 96, "end": 115}}, {"head": {"text": "LLMs", "start": 185, "end": 189}, "tail": {"text": "large language models", "start": 161, "end": 182}}], "used for": [{"head": {"text": "In-context learning", "start": 96, "end": 115}, "tail": {"text": "large language models", "start": 161, "end": 182}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , the underlying mechanism of how LLMs learn from the provided context remains under-explored .", "output": {"entities": {"method": [{"text": "LLMs", "start": 42, "end": 46}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we investigate the working mechanism of ICL through an information flow lens .", "output": {"entities": {"method": [{"text": "ICL", "start": 56, "end": 59}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our findings reveal that label words in the demonstration examples function as anchors : In-context Learning ( ICL ) has emerged as a powerful capability alongside the development of scaledup large language models ( LLMs ) ( Brown et al . , 2020 ) .", "output": {"entities": {"method": [{"text": "In-context Learning", "start": 89, "end": 108}, {"text": "ICL", "start": 111, "end": 114}, {"text": "large language models", "start": 192, "end": 213}, {"text": "LLMs", "start": 216, "end": 220}]}, "relations": {"synonym of": [{"head": {"text": "ICL", "start": 111, "end": 114}, "tail": {"text": "In-context Learning", "start": 89, "end": 108}}, {"head": {"text": "LLMs", "start": 216, "end": 220}, "tail": {"text": "large language models", "start": 192, "end": 213}}], "used for": [{"head": {"text": "In-context Learning", "start": 89, "end": 108}, "tail": {"text": "large language models", "start": 192, "end": 213}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By instructing LLMs using few-shot demonstration examples , ICL enables them to perform a wide range of tasks , such as text classification ( Min et al . , 2022a ) and mathematical reasoning ( Wei et al . , 2022 ) .", "output": {"entities": {"method": [{"text": "LLMs", "start": 15, "end": 19}, {"text": "ICL", "start": 60, "end": 63}], "task": [{"text": "text classification", "start": 120, "end": 139}, {"text": "mathematical reasoning", "start": 168, "end": 190}]}, "relations": {"used for": [{"head": {"text": "ICL", "start": 60, "end": 63}, "tail": {"text": "text classification", "start": 120, "end": 139}}, {"head": {"text": "ICL", "start": 60, "end": 63}, "tail": {"text": "mathematical reasoning", "start": 168, "end": 190}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Despite its significance , the inner working mechanism of ICL remains an open question , garnering considerable interest from research communities ( Xie et al . , 2022 ; Dai et al . , 2022 ; Akyürek et al . , 2022 ; Li et al . , 2023b ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 58, "end": 61}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we find that the label words serve as anchors that aggregate and distribute information in ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 107, "end": 110}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We first visualize the attention interactive pattern between tokens with a GPT model ( Brown et al . , 2020 ) on sentiment analysis ( Figure 1 ) .", "output": {"entities": {"method": [{"text": "attention interactive", "start": 23, "end": 44}, {"text": "GPT", "start": 75, "end": 78}], "task": [{"text": "sentiment analysis", "start": 113, "end": 131}]}, "relations": {"used for": [{"head": {"text": "GPT", "start": 75, "end": 78}, "tail": {"text": "sentiment analysis", "start": 113, "end": 131}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "H 2 : In deep layers , the model extracts the information from label words to form the final prediction .", "output": {"entities": {"method": [{"text": "deep layers", "start": 9, "end": 20}], "task": [{"text": "prediction", "start": 93, "end": 103}]}, "relations": {"used for": [{"head": {"text": "deep layers", "start": 9, "end": 20}, "tail": {"text": "prediction", "start": 93, "end": 103}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Two experiments are designed to validate the hypothesis using GPT2 - XL ( Radford et al . , 2019 ) and GPT-J ( Wang and Komatsuzaki , 2021 ) across several text classification benchmarks . ( 1 ) By blocking the information aggregation path to label words in certain layers , we find that such isolation in shallow layers significantly impairs model performance .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 62, "end": 71}, {"text": "GPT-J", "start": 103, "end": 108}, {"text": "shallow layers", "start": 306, "end": 320}], "task": [{"text": "text classification", "start": 156, "end": 175}]}, "relations": {"used for": [{"head": {"text": "GPT2 - XL", "start": 62, "end": 71}, "tail": {"text": "text classification", "start": 156, "end": 175}}, {"head": {"text": "GPT-J", "start": 103, "end": 108}, "tail": {"text": "text classification", "start": 156, "end": 175}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This indicates that label words collect useful information during forward propagation in shallow layers . ( 2 ) We investigate the relationship between the attention distributions on the label words of the target position and the model's final prediction .", "output": {"entities": {"method": [{"text": "forward propagation", "start": 66, "end": 85}, {"text": "shallow layers", "start": 89, "end": 103}]}, "relations": {"part of": [{"head": {"text": "forward propagation", "start": 66, "end": 85}, "tail": {"text": "shallow layers", "start": 89, "end": 103}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In summary , these experimental findings suggest that our hypothesis holds well with large language models on real-world datasets .", "output": {"entities": {"method": [{"text": "large language models", "start": 85, "end": 106}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Drawing on insights from the information flow perspective , we explore three approaches to enhance ICL's effectiveness , efficiency , and interpretability . ( 1 ) An anchor re-weighting method is introduced , which employs a learnable vector to adjust the significance of different label words in demonstrations , leading to a 16.7% average accuracy boost compared to standard ICL baselines . ( 2 ) For quicker ICL inference , inputs are compressed into pre-calculated anchor representations since model predictions primarily rely on label word activations .", "output": {"entities": {"method": [{"text": "ICL's", "start": 99, "end": 104}, {"text": "anchor re-weighting method", "start": 166, "end": 192}, {"text": "ICL", "start": 377, "end": 380}, {"text": "ICL", "start": 411, "end": 414}]}, "relations": {"compare with": [{"head": {"text": "anchor re-weighting method", "start": 166, "end": 192}, "tail": {"text": "ICL", "start": 377, "end": 380}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Testing shows a 1.8 × speedup in inference with only a minimal performance trade-off . ( 3 ) An error analysis of ICL on GPT2 - XL demonstrates that the label confusion matrix aligns closely with the distance distribution of anchor key vectors , implying that errors might result from similar anchor representations .", "output": {"entities": {"method": [{"text": "ICL", "start": 114, "end": 117}, {"text": "GPT2 - XL", "start": 121, "end": 130}]}, "relations": {"used for": [{"head": {"text": "ICL", "start": 114, "end": 117}, "tail": {"text": "GPT2 - XL", "start": 121, "end": 130}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These promising applications further validate our hypothesis and shed light on future ICL studies for better transparency of LLMs .", "output": {"entities": {"method": [{"text": "ICL", "start": 86, "end": 89}, {"text": "LLMs", "start": 125, "end": 129}]}, "relations": {"used for": [{"head": {"text": "ICL", "start": 86, "end": 89}, "tail": {"text": "LLMs", "start": 125, "end": 129}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "H 2 : In deep layers , the model makes predictions by extracting information from label words .", "output": {"entities": {"method": [{"text": "deep layers", "start": 9, "end": 20}], "task": [{"text": "predictions", "start": 39, "end": 50}]}, "relations": {"used for": [{"head": {"text": "deep layers", "start": 9, "end": 20}, "tail": {"text": "predictions", "start": 39, "end": 50}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This section aims to discover the inherent patterns in the attention interaction between tokens for a GPT model .", "output": {"entities": {"method": [{"text": "attention interaction", "start": 59, "end": 80}, {"text": "GPT", "start": 102, "end": 105}]}, "relations": {"part of": [{"head": {"text": "attention interaction", "start": 59, "end": 80}, "tail": {"text": "GPT", "start": 102, "end": 105}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Following common practice , we use the Taylor expansion ( Michel et al . , 2019 ) to calculate the saliency score for each element of the attention matrix : EQUATION Here , A h , l is the value of the attention matrix of the h-th attention head in the l-th layer , x is the input , and L ( x ) is the loss function of the task , e.g . , the cross-entropy objective for a classification problem .", "output": {"entities": {"method": [{"text": "Taylor expansion", "start": 39, "end": 55}, {"text": "loss function", "start": 301, "end": 314}, {"text": "cross-entropy objective", "start": 341, "end": 364}], "task": [{"text": "classification", "start": 371, "end": 385}]}, "relations": {"subclass of": [{"head": {"text": "cross-entropy objective", "start": 341, "end": 364}, "tail": {"text": "loss function", "start": 301, "end": 314}}], "used for": [{"head": {"text": "cross-entropy objective", "start": 341, "end": 364}, "tail": {"text": "classification", "start": 371, "end": 385}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We average all attention heads to obtain the saliency matrix I l for the l-th layer .", "output": {"entities": {"method": [{"text": "attention heads", "start": 15, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "I l ( i , j ) represents the significance of the information flow from the j-th word to the i-th word for ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 106, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By observing I l , we can get an intuitive impression that as the layer goes deeper , demonstration label words will become more dominant for the prediction , as depicted in Figure 1 .", "output": {"entities": {"task": [{"text": "prediction", "start": 146, "end": 156}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A high S pq demonstrates a strong information extraction from label words for final decision-making .", "output": {"entities": {"task": [{"text": "information extraction", "start": 34, "end": 56}, {"text": "decision-making", "start": 84, "end": 99}]}, "relations": {"used for": [{"head": {"text": "information extraction", "start": 34, "end": 56}, "tail": {"text": "decision-making", "start": 84, "end": 99}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Experimental Settings We choose GPT2 - XL from the GPT series ( Radford et al . , 2019 ) as our primary model for investigation , due to its moderate model size ( of 1.5B parameters ) that is suitable for our hardware resource and its decent ICL performance ( Dai et al . , 2022 ) .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 32, "end": 41}, {"text": "GPT", "start": 51, "end": 54}, {"text": "ICL", "start": 242, "end": 245}]}, "relations": {"subclass of": [{"head": {"text": "GPT2 - XL", "start": 32, "end": 41}, "tail": {"text": "GPT", "start": 51, "end": 54}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For datasets , we use Stanford Sentiment Treebank Binary ( SST - 2 ) ( Socher et al . , 2013 ) for sentiment analysis , Text REtrieval Conference Question Classification ( TREC ) ( Li and Roth , 2002 ; Hovy et al . , 2001 ) for question type classification , AG's news topic classification dataset ( AGNews ) ( Zhang et al . , 2015 ) for topic classification , and EmoContext ( EmoC ) ( Chatterjee et al . , 2019 ) Results and Analysis Figure 3 reveals that : ( 1 ) in shallow layers , S pq , the significance of the information flow from label words to targeted positions , is low , while S wp , the information flow from the text part to label words is high ; ( 2 ) in deep layers , S pq , the importance of information flow from label words to the targeted position becomes the dominant one .", "output": {"entities": {"dataset": [{"text": "Stanford Sentiment Treebank Binary", "start": 22, "end": 56}, {"text": "SST - 2", "start": 59, "end": 66}, {"text": "Text REtrieval Conference Question Classification", "start": 120, "end": 169}, {"text": "TREC", "start": 172, "end": 176}, {"text": "AG's news topic classification dataset", "start": 259, "end": 297}, {"text": "AGNews", "start": 300, "end": 306}, {"text": "EmoContext", "start": 365, "end": 375}, {"text": "EmoC", "start": 378, "end": 382}], "task": [{"text": "sentiment analysis", "start": 99, "end": 117}, {"text": "question type classification", "start": 228, "end": 256}, {"text": "topic classification", "start": 338, "end": 358}], "method": [{"text": "shallow layers", "start": 469, "end": 483}, {"text": "deep layers", "start": 671, "end": 682}]}, "relations": {"synonym of": [{"head": {"text": "SST - 2", "start": 59, "end": 66}, "tail": {"text": "Stanford Sentiment Treebank Binary", "start": 22, "end": 56}}, {"head": {"text": "TREC", "start": 172, "end": 176}, "tail": {"text": "Text REtrieval Conference Question Classification", "start": 120, "end": 169}}, {"head": {"text": "AGNews", "start": 300, "end": 306}, "tail": {"text": "AG's news topic classification dataset", "start": 259, "end": 297}}, {"head": {"text": "EmoC", "start": 378, "end": 382}, "tail": {"text": "EmoContext", "start": 365, "end": 375}}], "benchmark for": [{"head": {"text": "Stanford Sentiment Treebank Binary", "start": 22, "end": 56}, "tail": {"text": "sentiment analysis", "start": 99, "end": 117}}, {"head": {"text": "Text REtrieval Conference Question Classification", "start": 120, "end": 169}, "tail": {"text": "question type classification", "start": 228, "end": 256}}, {"head": {"text": "AG's news topic classification dataset", "start": 259, "end": 297}, "tail": {"text": "topic classification", "start": 338, "end": 358}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Proposed Hypothesis Based on this , we propose the hypothesis that label words function as anchors in the ICL information flow .", "output": {"entities": {"method": [{"text": "ICL", "start": 106, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In shallow layers , label words gather information from demonstration examples to form semantic representations for deeper layers , while in deep layers , the model extracts the information from label words to form the final prediction .", "output": {"entities": {"method": [{"text": "shallow layers", "start": 3, "end": 17}, {"text": "deeper layers", "start": 116, "end": 129}, {"text": "deep layers", "start": 141, "end": 152}], "task": [{"text": "prediction", "start": 225, "end": 235}]}, "relations": {"compare with": [{"head": {"text": "shallow layers", "start": 3, "end": 17}, "tail": {"text": "deep layers", "start": 141, "end": 152}}], "used for": [{"head": {"text": "deep layers", "start": 141, "end": 152}, "tail": {"text": "prediction", "start": 225, "end": 235}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We assume that the information aggregation in ICL relies on the information flow from the text part to label tokens , which is facilitated by the transformer's attention mechanism .", "output": {"entities": {"method": [{"text": "ICL", "start": 46, "end": 49}, {"text": "transformer's", "start": 146, "end": 159}, {"text": "attention mechanism", "start": 160, "end": 179}]}, "relations": {"part of": [{"head": {"text": "attention mechanism", "start": 160, "end": 179}, "tail": {"text": "transformer's", "start": 146, "end": 159}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By manipulating the attention layer in the model to block this flow and examining the model behavior change , we validate the existence of the information aggregation process and its contribution to the final prediction .", "output": {"entities": {"method": [{"text": "attention layer", "start": 20, "end": 35}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To further validate our findings on larger models , we incorporate GPT-J ( 6B ) ( Wang and Komatsuzaki , 2021 ) in experiments , which exceeds GPT2 - XL in model size and capacity .", "output": {"entities": {"method": [{"text": "GPT-J", "start": 67, "end": 72}, {"text": "GPT2 - XL", "start": 143, "end": 152}]}, "relations": {"compare with": [{"head": {"text": "GPT-J", "start": 67, "end": 72}, "tail": {"text": "GPT2 - XL", "start": 143, "end": 152}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Metrics We use the following metrics to assess the impact of blocking information flow from the text part to label tokens : ( 1 ) Label Loyalty : measures the consistency of output labels with and without isolation . ( 2 ) Word Loyalty : employs the Jaccard similarity to compare the top - 5 predicted words with and without isolation , capturing more subtle model output alterations ( See Appendix C for details ) .", "output": {"entities": {"method": [{"text": "Jaccard similarity", "start": 250, "end": 268}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Low loyalty indicates a profound impact of isolation on model predictions .", "output": {"entities": {"task": [{"text": "predictions", "start": 62, "end": 73}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Moreover , similar results were obtained when testing ICL with semantically unrelated labels ( refer to Appendix F .2 ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 54, "end": 57}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Figures 5a and 5b delineate correlation metrics for GPT2 - XL and GPT-J , averaged across four datasets .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 52, "end": 61}, {"text": "GPT-J", "start": 66, "end": 71}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The AUCROC l for deep layers approaches 0.8 , illustrating a strong correlation between the attention distributions on label words of the target position and the model's final prediction .", "output": {"entities": {"method": [{"text": "deep layers", "start": 17, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Moreover , shallow layers show negligible cumulative contributions ( R l ) , with a significant increase in middle and deep layers .", "output": {"entities": {"method": [{"text": "shallow layers", "start": 11, "end": 25}, {"text": "deep layers", "start": 119, "end": 130}]}, "relations": {"compare with": [{"head": {"text": "shallow layers", "start": 11, "end": 25}, "tail": {"text": "deep layers", "start": 119, "end": 130}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These results signify the crucial role of deep layers for final prediction , validating that the model extracts information from label words in deep layers to form the final prediction .", "output": {"entities": {"method": [{"text": "deep layers", "start": 42, "end": 53}, {"text": "deep layers", "start": 144, "end": 155}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In § 2.3 , we verify that the aforementioned aggregated information on label words is then extracted to form the final prediction in the deep layers .", "output": {"entities": {"method": [{"text": "deep layers", "start": 137, "end": 148}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Given the considerable role these \" anchors \" fulfill , we find it intuitive to design ICL improvements based on them , as elaborated in § 3 .", "output": {"entities": {"method": [{"text": "ICL", "start": 87, "end": 90}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "With insights from the validated hypothesis , we propose strategies to boost ICL's accuracy and inference speed .", "output": {"entities": {"method": [{"text": "ICL's", "start": 77, "end": 82}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We propose an anchor re-weighting method in § 3.1 to adjust the demonstrations ' contributions and improve accuracy .", "output": {"entities": {"method": [{"text": "anchor re-weighting method", "start": 14, "end": 40}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In § 3.2 , we explore a context compression technique that reduces original demonstrations to anchor hidden states to speed up ICL inference .", "output": {"entities": {"method": [{"text": "ICL", "start": 127, "end": 130}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Besides , in § 3.3 , we utilize anchor distances to perform an analysis to understand the errors ICL made in real-world scenarios .", "output": {"entities": {"method": [{"text": "ICL", "start": 97, "end": 100}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These approaches corroborate our hypothesis , pointing to potential paths for future ICL enhancements .", "output": {"entities": {"method": [{"text": "ICL", "start": 85, "end": 88}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Based on our analysis in § 2 , we draw parallels between ICL and logistic regression and propose an approach to improve ICL's accuracy by reweighting label anchors . 3.1.1 Method § 2.3 illustrates a strong correlation between the model's output category and the attention distribution ( A ( q , p 1 ) , . . . , A ( q , p C ) ) on label words p 1 , . . . , p C of the target position q in deep layers .", "output": {"entities": {"method": [{"text": "ICL", "start": 57, "end": 60}, {"text": "logistic regression", "start": 65, "end": 84}, {"text": "ICL's", "start": 120, "end": 125}, {"text": "reweighting label anchors", "start": 138, "end": 163}, {"text": "attention distribution", "start": 262, "end": 284}, {"text": "deep layers", "start": 388, "end": 399}]}, "relations": {"compare with": [{"head": {"text": "ICL", "start": 57, "end": 60}, "tail": {"text": "logistic regression", "start": 65, "end": 84}}], "part of": [{"head": {"text": "reweighting label anchors", "start": 138, "end": 163}, "tail": {"text": "ICL's", "start": 120, "end": 125}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Inspired by the similarity between ICL and logistic regression , we've incorporated a learnable β i 0 into Eq . ( 7 ) , which is equivalent to adjusting the attention weights A ( q , p i ) : EQUATION Each β i 0 is a learnable parameter , set uniquely for different attention heads and layers .", "output": {"entities": {"method": [{"text": "ICL", "start": 35, "end": 38}, {"text": "logistic regression", "start": 43, "end": 62}, {"text": "attention heads", "start": 265, "end": 280}]}, "relations": {"compare with": [{"head": {"text": "ICL", "start": 35, "end": 38}, "tail": {"text": "logistic regression", "start": 43, "end": 62}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Owing to computational constraints , we employ GPT2 - XL for evaluation , excluding GPT-J .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 47, "end": 56}, {"text": "GPT-J", "start": 84, "end": 89}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We compare Anchoring Re-weighting with two baselines : ( 1 ) Vanilla ICL with the same demonstration ( 1 - shot per class ) ( 2 ) Vanilla ICL , where the auxiliary training set of β is included as demonstrations ( 5 - shot per class ) for a fair comparison .", "output": {"entities": {"method": [{"text": "Anchoring Re-weighting", "start": 11, "end": 33}, {"text": "ICL", "start": 69, "end": 72}, {"text": "ICL", "start": 138, "end": 141}]}, "relations": {"compare with": [{"head": {"text": "Anchoring Re-weighting", "start": 11, "end": 33}, "tail": {"text": "ICL", "start": 69, "end": 72}}, {"head": {"text": "Anchoring Re-weighting", "start": 11, "end": 33}, "tail": {"text": "ICL", "start": 138, "end": 141}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As Table 1 shows , the proposed anchor reweighting significantly enhances ICL performance , particularly on the SST - 2 and EmoC datasets .", "output": {"entities": {"method": [{"text": "ICL", "start": 74, "end": 77}], "dataset": [{"text": "SST - 2", "start": 112, "end": 119}, {"text": "EmoC", "start": 124, "end": 128}]}, "relations": {"evaluated with": [{"head": {"text": "ICL", "start": 74, "end": 77}, "tail": {"text": "SST - 2", "start": 112, "end": 119}}, {"head": {"text": "ICL", "start": 74, "end": 77}, "tail": {"text": "EmoC", "start": 124, "end": 128}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Besides , adding more demonstrations for vanilla ICL may not bring a stable accuracy boost due to the potential noise introduced , as discussed in Zhao et al . ( 2021 ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 49, "end": 52}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Different from vanilla ICL which utilizes the extra examples to form a demonstration , we train a re-weighting vector β to modulate label anchor contributions .", "output": {"entities": {"method": [{"text": "ICL", "start": 23, "end": 26}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The consistent improvements of our method suggest that the re-weighting mechanism could be a better alternative to utilize demonstration examples .", "output": {"entities": {"method": [{"text": "re-weighting mechanism", "start": 59, "end": 81}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Furthermore , it reiterates the crucial role that anchors play in ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 66, "end": 69}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We further explore a context compression technique that reduces the full demonstration to anchor hidden states for accelerating ICL inference .", "output": {"entities": {"method": [{"text": "ICL", "start": 128, "end": 131}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For AGNews , due to the length limit , we only use three demonstrations per class .", "output": {"entities": {"dataset": [{"text": "AGNews", "start": 4, "end": 10}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our Anchor Re-weighting method achieves the best performance overall tasks . from the demonstrations .", "output": {"entities": {"method": [{"text": "Anchor Re-weighting", "start": 4, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Given the auto-regressive nature of GPT-like models , where hidden states of tokens depend solely on preceding ones , label words ' information aggregation process is independent of subsequent words .", "output": {"entities": {"method": [{"text": "auto-regressive", "start": 10, "end": 25}, {"text": "GPT-like models", "start": 36, "end": 51}]}, "relations": {"part of": [{"head": {"text": "auto-regressive", "start": 10, "end": 25}, "tail": {"text": "GPT-like models", "start": 36, "end": 51}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In our preliminary experiments , concatenating hidden states of label words alone was inadequate for completing the ICL task . 5 This might be due to the critical role of formatting information in helping the model to determine the output space at the target position , 6 as highlighted in Min et al . ( 2022b ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 116, "end": 119}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As a solution , we amalgamate the hidden states of both the formatting and the label words , a method we've termed Hidden anchor .", "output": {"entities": {"method": [{"text": "Hidden anchor", "start": 115, "end": 128}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We compare our Hidden anchor input compression method with two equally efficient baselines .", "output": {"entities": {"method": [{"text": "Hidden anchor", "start": 15, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Text anchor : This method concatenates the formatting and label text with the input , as opposed to concatenating the hidden states at each layer .", "output": {"entities": {"method": [{"text": "Text anchor", "start": 0, "end": 11}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Hidden random : This approach concatenates the hidden states of formatting and randomly selected nonlabel words ( equal in number to Hidden anchor ) .", "output": {"entities": {"method": [{"text": "Hidden random", "start": 0, "end": 13}, {"text": "Hidden anchor", "start": 133, "end": 146}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Hidden random-top : To establish a stronger baseline , we randomly select 20 sets of non-label words in Hidden random and report the one with the highest label loyalty .", "output": {"entities": {"method": [{"text": "Hidden random-top", "start": 0, "end": 17}, {"text": "Hidden random", "start": 104, "end": 117}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The Text anchor method is included to demonstrate that the effectiveness of Hidden anchor is attributed to the aggregation of information in label words , rather than the mere text of label words .", "output": {"entities": {"method": [{"text": "Text anchor", "start": 4, "end": 15}, {"text": "Hidden anchor", "start": 76, "end": 89}]}, "relations": {"compare with": [{"head": {"text": "Text anchor", "start": 4, "end": 15}, "tail": {"text": "Hidden anchor", "start": 76, "end": 89}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "If we find that Hidden anchor surpasses Text anchor in performance , it solidifies the notion that the aggregated information within label words carries significant importance .", "output": {"entities": {"method": [{"text": "Hidden anchor", "start": 16, "end": 29}, {"text": "Text anchor", "start": 40, "end": 51}]}, "relations": {"compare with": [{"head": {"text": "Hidden anchor", "start": 16, "end": 29}, "tail": {"text": "Text anchor", "start": 40, "end": 51}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The Hidden random method is introduced to illustrate that anchor hidden states encapsulate most of the demonstration information among all hidden states .", "output": {"entities": {"method": [{"text": "Hidden random", "start": 4, "end": 17}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We can see from Table 2 that the proposed compression method Hidden anchor achieves the best results among all three compression methods on all metrics and for both models .", "output": {"entities": {"method": [{"text": "Hidden anchor", "start": 61, "end": 74}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For example , with the GPT-J model , the compression method with anchor states only leads to a 1.5 accuracy drop compared to the uncompressed situation , indicating that the compression introduces negligible information loss .", "output": {"entities": {"method": [{"text": "GPT-J", "start": 23, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Further , we estimate the efficiency improvements over the original ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 68, "end": 71}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Besides , we observe that the acceleration effect is more pronounced in the GPT-J model compared to GPT2 - XL , demonstrating its great potential to apply to larger language models .", "output": {"entities": {"method": [{"text": "GPT-J", "start": 76, "end": 81}, {"text": "GPT2 - XL", "start": 100, "end": 109}, {"text": "larger language models", "start": 158, "end": 180}]}, "relations": {"compare with": [{"head": {"text": "GPT-J", "start": 76, "end": 81}, "tail": {"text": "GPT2 - XL", "start": 100, "end": 109}}], "subclass of": [{"head": {"text": "GPT2 - XL", "start": 100, "end": 109}, "tail": {"text": "larger language models", "start": 158, "end": 180}}, {"head": {"text": "GPT-J", "start": 76, "end": 81}, "tail": {"text": "larger language models", "start": 158, "end": 180}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Lastly , we perform an error analysis for ICL by examining the distances between the key vectors in the attention module that correspond to the label words .", "output": {"entities": {"method": [{"text": "ICL", "start": 42, "end": 45}, {"text": "attention module", "start": 104, "end": 120}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Furthermore , considering the distribution of query vectors q q , we employ a PCA-like method to extract the components of the key vectors along the directions with significant variations in q q , denoted as k ( see Appendix J for details ) .", "output": {"entities": {"method": [{"text": "PCA-like", "start": 78, "end": 86}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We anticipate that the distances between these ks can correspond to the category confusion of the model , thus revealing one possible origin of ICL errors .", "output": {"entities": {"method": [{"text": "ICL", "start": 144, "end": 147}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , we normalize the distances to a scale of 0 - 1 , with 0 indicating the highest degree of category confusion : EQUATION We utilize the GPT2 - XL model and TREC dataset , as the model displays varying confusion levels between categories on this dataset .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 141, "end": 150}], "dataset": [{"text": "TREC", "start": 161, "end": 165}]}, "relations": {"evaluated with": [{"head": {"text": "GPT2 - XL", "start": 141, "end": 150}, "tail": {"text": "TREC", "start": 161, "end": 165}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We use all 500 samples of the TREC test set and use 1 demonstration per class for convenience of analysis .", "output": {"entities": {"dataset": [{"text": "TREC", "start": 30, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The heatmaps display similarity in confusing category pairs , particularly in lighter-colored blocks .", "output": {"entities": {"method": [{"text": "lighter-colored blocks", "start": 78, "end": 100}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This high correlation indicates that ICL makes errors in categories with similar label anchors .", "output": {"entities": {"method": [{"text": "ICL", "start": 37, "end": 40}, {"text": "label anchors", "start": 81, "end": 94}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The existing literature on in-context learning analysis can be broadly divided into two streams , each focusing on different aspects .", "output": {"entities": {"method": [{"text": "in-context learning", "start": 27, "end": 46}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The first stream explores the influencing factors of ICL based on input perturbation , such as the order ( Min et al . , 2022b ) , the formatting ( Yoo et al . , 2022 ; Wei et al . , 2022 ) , and the selection of the demonstration ( Liu et al . , 2022 ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 53, "end": 56}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Designing proper demonstration construc-tion strategies ( Ye et al . , 2023 ; Li et al . , 2023a ) and calibration techniques ( Zhao et al . , 2021 ; Min et al . , 2022a ) could bring clear boosts to the ICL performance .", "output": {"entities": {"method": [{"text": "demonstration construc-tion strategies", "start": 17, "end": 55}, {"text": "calibration", "start": 103, "end": 114}, {"text": "ICL", "start": 204, "end": 207}]}, "relations": {"used for": [{"head": {"text": "demonstration construc-tion strategies", "start": 17, "end": 55}, "tail": {"text": "ICL", "start": 204, "end": 207}}, {"head": {"text": "calibration", "start": 103, "end": 114}, "tail": {"text": "ICL", "start": 204, "end": 207}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The second stream investigates the inner working mechanism of ICL through different conceptual lenses , such as making an analogy of ICL to gradient descent ( von Oswald et al . , 2022 ; Dai et al . , 2022 ) and viewing the process of ICL as a Bayesian inference ( Xie et al . , 2022 ) .", "output": {"entities": {"method": [{"text": "ICL", "start": 62, "end": 65}, {"text": "ICL", "start": 133, "end": 136}, {"text": "gradient descent", "start": 140, "end": 156}, {"text": "ICL", "start": 235, "end": 238}, {"text": "Bayesian inference", "start": 244, "end": 262}]}, "relations": {"compare with": [{"head": {"text": "ICL", "start": 133, "end": 136}, "tail": {"text": "gradient descent", "start": 140, "end": 156}}, {"head": {"text": "ICL", "start": 235, "end": 238}, "tail": {"text": "Bayesian inference", "start": 244, "end": 262}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we provide a novel perspective by examining the information flow in language models to gain an understanding of ICL .", "output": {"entities": {"method": [{"text": "language models", "start": 84, "end": 99}, {"text": "ICL", "start": 128, "end": 131}]}, "relations": {"used for": [{"head": {"text": "ICL", "start": 128, "end": 131}, "tail": {"text": "language models", "start": 84, "end": 99}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our approach offers new insights and demonstrates the potential for leveraging this understanding to improve the effectiveness , efficiency , and interpretability of ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 166, "end": 169}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we propose a hypothesis that label words serve as anchors in in-context learning for aggregating and distributing the task-relevant information flow .", "output": {"entities": {"method": [{"text": "in-context learning", "start": 77, "end": 96}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Experimental results with attention manipulation and analysis of predictions correlation consolidate the hypothesis holds well in GPT2 - XL and GPT-J models .", "output": {"entities": {"method": [{"text": "attention manipulation", "start": 26, "end": 48}, {"text": "predictions correlation", "start": 65, "end": 88}, {"text": "GPT2 - XL", "start": 130, "end": 139}, {"text": "GPT-J", "start": 144, "end": 149}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "First , an anchor re-weighting method is proposed to improve ICL accuracy .", "output": {"entities": {"method": [{"text": "anchor re-weighting method", "start": 11, "end": 37}, {"text": "ICL", "start": 61, "end": 64}]}, "relations": {"part of": [{"head": {"text": "anchor re-weighting method", "start": 11, "end": 37}, "tail": {"text": "ICL", "start": 61, "end": 64}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Second , we explore a demonstration compression technique to accelerate ICL inference .", "output": {"entities": {"method": [{"text": "ICL", "start": 72, "end": 75}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Lastly , we showcase an analysis framework to diagnose ICL errors on a real-world dataset .", "output": {"entities": {"method": [{"text": "ICL", "start": 55, "end": 58}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These promising applications again verify the hypothesis and open up new directions for future investigations on ICL .", "output": {"entities": {"method": [{"text": "ICL", "start": 113, "end": 116}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our study , while providing valuable insights into in-context learning ( ICL ) , has several limitations .", "output": {"entities": {"method": [{"text": "in-context learning", "start": 51, "end": 70}, {"text": "ICL", "start": 73, "end": 76}]}, "relations": {"synonym of": [{"head": {"text": "ICL", "start": 73, "end": 76}, "tail": {"text": "in-context learning", "start": 51, "end": 70}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , our hypothesis was only examined within conventional ICL paradigms , leaving other ICL paradigms such as the chain of thought prompting ( CoT ) ( Wei et al . , 2022 ) unexplored .", "output": {"entities": {"method": [{"text": "ICL", "start": 68, "end": 71}, {"text": "ICL", "start": 98, "end": 101}, {"text": "chain of thought prompting", "start": 124, "end": 150}, {"text": "CoT", "start": 153, "end": 156}]}, "relations": {"part of": [{"head": {"text": "chain of thought prompting", "start": 124, "end": 150}, "tail": {"text": "ICL", "start": 98, "end": 101}}], "synonym of": [{"head": {"text": "CoT", "start": 153, "end": 156}, "tail": {"text": "chain of thought prompting", "start": 124, "end": 150}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For models , we use GPT2 - XL ( 1.5B ) ( Radford et al . , 2019 ) and GPT-J ( 6B ) ( Wang and Komatsuzaki , 2021 ) in this paper .", "output": {"entities": {"method": [{"text": "GPT2 - XL", "start": 20, "end": 29}, {"text": "GPT-J", "start": 70, "end": 75}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For datasets , we use a sentiment analysis task , Stanford Sentiment Treebank Binary ( SST - 2 ) ( Socher et al . , 2013 ) , a question type classification task , Text REtrieval Conference Question Classification ( TREC ) ( Li and Roth , 2002 ; Hovy et al . , 2001 ) , a topic classification task , AG's news topic classification dataset ( AGNews ) ( Zhang et al . , 2015 ) , and an emotion classification task , Emo-Context ( EmoC ) ( Chatterjee et al . , 2019 ) .", "output": {"entities": {"task": [{"text": "sentiment analysis", "start": 24, "end": 42}, {"text": "question type classification", "start": 127, "end": 155}, {"text": "topic classification", "start": 271, "end": 291}, {"text": "emotion classification", "start": 383, "end": 405}], "dataset": [{"text": "Stanford Sentiment Treebank Binary", "start": 50, "end": 84}, {"text": "SST - 2", "start": 87, "end": 94}, {"text": "Text REtrieval Conference Question Classification", "start": 163, "end": 212}, {"text": "TREC", "start": 215, "end": 219}, {"text": "AG's news topic classification dataset", "start": 299, "end": 337}, {"text": "AGNews", "start": 340, "end": 346}, {"text": "Emo-Context", "start": 413, "end": 424}, {"text": "EmoC", "start": 427, "end": 431}]}, "relations": {"benchmark for": [{"head": {"text": "Stanford Sentiment Treebank Binary", "start": 50, "end": 84}, "tail": {"text": "sentiment analysis", "start": 24, "end": 42}}, {"head": {"text": "Text REtrieval Conference Question Classification", "start": 163, "end": 212}, "tail": {"text": "question type classification", "start": 127, "end": 155}}, {"head": {"text": "AG's news topic classification dataset", "start": 299, "end": 337}, "tail": {"text": "topic classification", "start": 271, "end": 291}}, {"head": {"text": "Emo-Context", "start": 413, "end": 424}, "tail": {"text": "emotion classification", "start": 383, "end": 405}}], "synonym of": [{"head": {"text": "SST - 2", "start": 87, "end": 94}, "tail": {"text": "Stanford Sentiment Treebank Binary", "start": 50, "end": 84}}, {"head": {"text": "TREC", "start": 215, "end": 219}, "tail": {"text": "Text REtrieval Conference Question Classification", "start": 163, "end": 212}}, {"head": {"text": "AGNews", "start": 340, "end": 346}, "tail": {"text": "AG's news topic classification dataset", "start": 299, "end": 337}}, {"head": {"text": "EmoC", "start": 427, "end": 431}, "tail": {"text": "Emo-Context", "start": 413, "end": 424}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. 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{"input": "A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics In drug discovery , molecular dynamics ( MD ) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities , estimating transport properties , and exploring pocket sites .", "output": {"entities": {"method": [{"text": "Multi-Grained Symmetric Differential Equation", "start": 2, "end": 47}, {"text": "molecular dynamics", "start": 119, "end": 137}, {"text": "MD", "start": 140, "end": 142}], "task": [{"text": "Learning Protein-Ligand Binding Dynamics", "start": 58, "end": 98}, {"text": "drug discovery", "start": 102, "end": 116}, {"text": "protein-ligand binding", "start": 160, "end": 182}, {"text": "predicting binding affinities", "start": 212, "end": 241}, {"text": "estimating transport properties", "start": 244, "end": 275}, {"text": "exploring pocket sites", "start": 282, "end": 304}]}, "relations": {"used for": [{"head": {"text": "Multi-Grained Symmetric Differential Equation", "start": 2, "end": 47}, "tail": {"text": "Learning Protein-Ligand Binding Dynamics", "start": 58, "end": 98}}, {"head": {"text": "Multi-Grained Symmetric Differential Equation", "start": 2, "end": 47}, "tail": {"text": "drug discovery", "start": 102, "end": 116}}, {"head": {"text": "molecular dynamics", "start": 119, "end": 137}, "tail": {"text": "protein-ligand binding", "start": 160, "end": 182}}, {"head": {"text": "molecular dynamics", "start": 119, "end": 137}, "tail": {"text": "predicting binding affinities", "start": 212, "end": 241}}, {"head": {"text": "molecular dynamics", "start": 119, "end": 137}, "tail": {"text": "estimating transport properties", "start": 244, "end": 275}}, {"head": {"text": "molecular dynamics", "start": 119, "end": 137}, "tail": {"text": "exploring pocket sites", "start": 282, "end": 304}}], "subtask of": [{"head": {"text": "Learning Protein-Ligand Binding Dynamics", "start": 58, "end": 98}, "tail": {"text": "drug discovery", "start": 102, "end": 116}}], "synonym of": [{"head": {"text": "MD", "start": 140, "end": 142}, "tail": {"text": "molecular dynamics", "start": 119, "end": 137}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. 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{"input": "There has been a long history of improving the efficiency of MD simulations through better numerical methods and , more recently , by utilizing machine learning ( ML ) methods .", "output": {"entities": {"task": [{"text": "MD simulations", "start": 61, "end": 75}], "method": [{"text": "machine learning", "start": 144, "end": 160}, {"text": "ML", "start": 163, "end": 165}]}, "relations": {"used for": [{"head": {"text": "machine learning", "start": 144, "end": 160}, "tail": {"text": "MD simulations", "start": 61, "end": 75}}], "synonym of": [{"head": {"text": "ML", "start": 163, "end": 165}, "tail": {"text": "machine learning", "start": 144, "end": 160}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To address this issue , we propose NeuralMD , the first ML surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 35, "end": 43}, {"text": "ML", "start": 56, "end": 58}, {"text": "numerical MD", "start": 89, "end": 101}], "task": [{"text": "protein-ligand binding", "start": 138, "end": 160}]}, "relations": {"subclass of": [{"head": {"text": "NeuralMD", "start": 35, "end": 43}, "tail": {"text": "ML", "start": 56, "end": 58}}], "used for": [{"head": {"text": "NeuralMD", "start": 35, "end": 43}, "tail": {"text": "protein-ligand binding", "start": 138, "end": 160}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Specifically , we propose ( 1 ) a BindingNet model that satisfies group symmetry using vector frames and captures the multi-level protein-ligand interactions , and ( 2 ) an augmented neural differential equation solver that learns the trajectory under Newtonian mechanics .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 34, "end": 44}, {"text": "neural differential equation solver", "start": 183, "end": 218}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For the experiment , we design ten single-trajectory and three multi-trajectory binding simulation tasks .", "output": {"entities": {"task": [{"text": "single-trajectory", "start": 35, "end": 52}, {"text": "multi-trajectory binding simulation", "start": 63, "end": 98}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We show the efficiency and effectiveness of NeuralMD , with a 2000 × speedup over standard numerical MD simulation and outperforming all other ML approaches by up to ~ 80% under the stability metric .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 44, "end": 52}, {"text": "ML", "start": 143, "end": 145}], "task": [{"text": "numerical MD simulation", "start": 91, "end": 114}]}, "relations": {"compare with": [{"head": {"text": "NeuralMD", "start": 44, "end": 52}, "tail": {"text": "ML", "start": 143, "end": 145}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We further qualitatively show that NeuralMD reaches more stable binding predictions compared to other machine learning methods .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 35, "end": 43}, {"text": "machine learning", "start": 102, "end": 118}]}, "relations": {"compare with": [{"head": {"text": "NeuralMD", "start": 35, "end": 43}, "tail": {"text": "machine learning", "start": 102, "end": 118}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The molecular dynamics ( MD ) simulation for protein-ligand binding is one of the fundamental tasks in drug discovery [ 1 , 2 , 3 , 4 ] .", "output": {"entities": {"method": [{"text": "molecular dynamics", "start": 4, "end": 22}, {"text": "MD", "start": 25, "end": 27}], "task": [{"text": "protein-ligand binding", "start": 45, "end": 67}, {"text": "drug discovery", "start": 103, "end": 117}]}, "relations": {"synonym of": [{"head": {"text": "MD", "start": 25, "end": 27}, "tail": {"text": "molecular dynamics", "start": 4, "end": 22}}], "used for": [{"head": {"text": "molecular dynamics", "start": 4, "end": 22}, "tail": {"text": "protein-ligand binding", "start": 45, "end": 67}}, {"head": {"text": "molecular dynamics", "start": 4, "end": 22}, "tail": {"text": "drug discovery", "start": 103, "end": 117}}], "subtask of": [{"head": {"text": "protein-ligand binding", "start": 45, "end": 67}, "tail": {"text": "drug discovery", "start": 103, "end": 117}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To simulate the protein-ligand dynamics , numerical MD methods have been extensively developed [ 5 , 6 ] .", "output": {"entities": {"task": [{"text": "protein-ligand dynamics", "start": 16, "end": 39}], "method": [{"text": "numerical MD", "start": 42, "end": 54}]}, "relations": {"used for": [{"head": {"text": "numerical MD", "start": 42, "end": 54}, "tail": {"text": "protein-ligand dynamics", "start": 16, "end": 39}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To alleviate this issue , machine learning ( ML ) surrogates have been proposed to either augment or replace numerical MD methods to estimate the MD trajectories .", "output": {"entities": {"method": [{"text": "machine learning", "start": 26, "end": 42}, {"text": "ML", "start": 45, "end": 47}, {"text": "numerical MD", "start": 109, "end": 121}], "task": [{"text": "estimate the MD trajectories", "start": 133, "end": 161}]}, "relations": {"synonym of": [{"head": {"text": "ML", "start": 45, "end": 47}, "tail": {"text": "machine learning", "start": 26, "end": 42}}], "used for": [{"head": {"text": "machine learning", "start": 26, "end": 42}, "tail": {"text": "estimate the MD trajectories", "start": 133, "end": 161}}, {"head": {"text": "numerical MD", "start": 109, "end": 121}, "tail": {"text": "estimate the MD trajectories", "start": 133, "end": 161}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , all prior ML approaches for MD simulation are limited to single-system ( e.g . , either small molecules or proteins ) and not protein-ligand complex [ 7 , 8 , 9 ] .", "output": {"entities": {"method": [{"text": "ML", "start": 20, "end": 22}], "task": [{"text": "MD simulation", "start": 38, "end": 51}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 20, "end": 22}, "tail": {"text": "MD simulation", "start": 38, "end": 51}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A primary reason is the lack of large-scale datasets for protein-ligand binding .", "output": {"entities": {"task": [{"text": "protein-ligand binding", "start": 57, "end": 79}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Another critical aspect that needs to be considered in ML-based modeling is the group symmetry present in the proteinligand geometry .", "output": {"entities": {"method": [{"text": "ML-based modeling", "start": 55, "end": 72}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Specifically , the geometric function over molecular systems should be equivariant to rotation and translation , i.e . , SE ( 3 ) - equivariance .", "output": {"entities": {"method": [{"text": "molecular systems", "start": 43, "end": 60}, {"text": "SE ( 3 ) - equivariance", "start": 121, "end": 144}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "DenoisingLD , exhibiting a lower degree of torsion with the natural conformations .", "output": {"entities": {"method": [{"text": "DenoisingLD", "start": 0, "end": 11}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Other methods collapse heavily , including GNN-MD and VerletMD , where atoms extend beyond the frame for the latter .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 43, "end": 49}, {"text": "VerletMD", "start": 54, "end": 62}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our Approach : NeuralMD .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 15, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Further , our ML approach NeuralMD preserves the Newtonian mechanics .", "output": {"entities": {"method": [{"text": "ML", "start": 14, "end": 16}, {"text": "NeuralMD", "start": 26, "end": 34}]}, "relations": {"subclass of": [{"head": {"text": "NeuralMD", "start": 26, "end": 34}, "tail": {"text": "ML", "start": 14, "end": 16}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In MD , the movement of atoms is determined by Newton's second law , F = m • a , where F is the force , m is the mass , and a is the acceleration of each atom .", "output": {"entities": {"method": [{"text": "MD", "start": 3, "end": 5}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Thus in NeuralMD , we formulate the trajectory simulation as a second-order ordinary differential equation ( ODE ) or second-order stochastic differential equation ( SDE ) problem .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 8, "end": 16}, {"text": "second-order ordinary differential equation", "start": 63, "end": 106}, {"text": "ODE", "start": 109, "end": 112}, {"text": "second-order stochastic differential equation", "start": 118, "end": 163}, {"text": "SDE", "start": 166, "end": 169}], "task": [{"text": "trajectory simulation", "start": 36, "end": 57}]}, "relations": {"part of": [{"head": {"text": "second-order stochastic differential equation", "start": 118, "end": 163}, "tail": {"text": "NeuralMD", "start": 8, "end": 16}}, {"head": {"text": "second-order ordinary differential equation", "start": 63, "end": 106}, "tail": {"text": "NeuralMD", "start": 8, "end": 16}}], "used for": [{"head": {"text": "second-order ordinary differential equation", "start": 63, "end": 106}, "tail": {"text": "trajectory simulation", "start": 36, "end": 57}}, {"head": {"text": "second-order stochastic differential equation", "start": 118, "end": 163}, "tail": {"text": "trajectory simulation", "start": 36, "end": 57}}], "synonym of": [{"head": {"text": "ODE", "start": 109, "end": 112}, "tail": {"text": "second-order ordinary differential equation", "start": 63, "end": 106}}, {"head": {"text": "SDE", "start": 166, "end": 169}, "tail": {"text": "second-order stochastic differential equation", "start": 118, "end": 163}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To verify the effectiveness and efficiency of NeuralMD , we design ten single-trajectory and three multitrajectory binding simulation tasks .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 46, "end": 54}], "task": [{"text": "single-trajectory", "start": 71, "end": 88}, {"text": "multitrajectory binding simulation", "start": 99, "end": 133}]}, "relations": {"used for": [{"head": {"text": "NeuralMD", "start": 46, "end": 54}, "tail": {"text": "single-trajectory", "start": 71, "end": 88}}, {"head": {"text": "NeuralMD", "start": 46, "end": 54}, "tail": {"text": "multitrajectory binding simulation", "start": 99, "end": 133}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We observe that NeuralMD outperforms all other ML methods [ 9 , 23 , 24 , 25 , 26 ] on 12 tasks using recovery metric , and NeuralMD is consistently better by a large gap using the stability metric ( up to ~ 80% ) .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 16, "end": 24}, {"text": "ML", "start": 47, "end": 49}, {"text": "NeuralMD", "start": 124, "end": 132}]}, "relations": {"compare with": [{"head": {"text": "NeuralMD", "start": 16, "end": 24}, "tail": {"text": "ML", "start": 47, "end": 49}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "They are three protein-ligand binding complexes from Protein Data Bank ( PDB ) , as shown in Figure 1 .", "output": {"entities": {"dataset": [{"text": "Protein Data Bank", "start": 53, "end": 70}, {"text": "PDB", "start": 73, "end": 76}]}, "relations": {"synonym of": [{"head": {"text": "PDB", "start": 73, "end": 76}, "tail": {"text": "Protein Data Bank", "start": 53, "end": 70}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In addition to the backbone level , as a coarser-grained view , we further consider residue-level information for modeling binding interactions , { f ( p ) , x x x ( p ) } , where the coordinate of C α is taken as the residue-level coordinate , i.e . , x x x ( p ) ≜ x x x ( p ) C α .", "output": {"entities": {"task": [{"text": "binding interactions", "start": 123, "end": 143}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Molecular Dynamics Simulations .", "output": {"entities": {"task": [{"text": "Molecular Dynamics Simulations", "start": 0, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Generally , molecular dynamics ( MD ) describes how each atom in a molecular system moves over time , following Newton's second law of motion : EQUATION where F is the force , m is the mass , a is the acceleration , x x x is the position , and t is the time .", "output": {"entities": {"method": [{"text": "molecular dynamics", "start": 12, "end": 30}, {"text": "MD", "start": 33, "end": 35}]}, "relations": {"synonym of": [{"head": {"text": "MD", "start": 33, "end": 35}, "tail": {"text": "molecular dynamics", "start": 12, "end": 30}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The numerical MD methods can be classified into classical MD and ab-initio MD , where the difference lies in how the force on each atom is calculated : classical MD uses force field approaches to predict the atomic forces [ 5 ] , while ab-initio MD calculates the forces using quantum mechanical methods , such as density functional theory ( DFT ) [ 6 ] .", "output": {"entities": {"method": [{"text": "numerical MD", "start": 4, "end": 16}, {"text": "classical MD", "start": 48, "end": 60}, {"text": "ab-initio MD", "start": 65, "end": 77}, {"text": "classical MD", "start": 152, "end": 164}, {"text": "ab-initio MD", "start": 236, "end": 248}, {"text": "quantum mechanical", "start": 277, "end": 295}, {"text": "density functional theory", "start": 314, "end": 339}, {"text": "DFT", "start": 342, "end": 345}], "task": [{"text": "predict the atomic forces", "start": 196, "end": 221}]}, "relations": {"subclass of": [{"head": {"text": "classical MD", "start": 48, "end": 60}, "tail": {"text": "numerical MD", "start": 4, "end": 16}}, {"head": {"text": "ab-initio MD", "start": 65, "end": 77}, "tail": {"text": "numerical MD", "start": 4, "end": 16}}, {"head": {"text": "density functional theory", "start": 314, "end": 339}, "tail": {"text": "quantum mechanical", "start": 277, "end": 295}}], "used for": [{"head": {"text": "classical MD", "start": 152, "end": 164}, "tail": {"text": "predict the atomic forces", "start": 196, "end": 221}}, {"head": {"text": "ab-initio MD", "start": 236, "end": 248}, "tail": {"text": "predict the atomic forces", "start": 196, "end": 221}}], "part of": [{"head": {"text": "density functional theory", "start": 314, "end": 339}, "tail": {"text": "ab-initio MD", "start": 236, "end": 248}}, {"head": {"text": "quantum mechanical", "start": 277, "end": 295}, "tail": {"text": "ab-initio MD", "start": 236, "end": 248}}], "compare with": [{"head": {"text": "classical MD", "start": 152, "end": 164}, "tail": {"text": "ab-initio MD", "start": 236, "end": 248}}], "synonym of": [{"head": {"text": "DFT", "start": 342, "end": 345}, "tail": {"text": "density functional theory", "start": 314, "end": 339}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. 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{"input": "More recently , ML MD methods have opened a new perspective by utilizing the group symmetric tools for geometric representation and energy prediction [ 11 , 12 , 13 , 14 , 15 , 17 , 18 , 19 , 27 ] , as well as the automatic differential tools for trajectory learning [ 28 , 29 , 30 , 31 , 32 , 33 ] .", "output": {"entities": {"method": [{"text": "ML MD methods", "start": 16, "end": 29}, {"text": "automatic differential tools", "start": 214, "end": 242}], "task": [{"text": "geometric representation", "start": 103, "end": 127}, {"text": "energy prediction", "start": 132, "end": 149}, {"text": "trajectory learning", "start": 247, "end": 266}]}, "relations": {"used for": [{"head": {"text": "ML MD methods", "start": 16, "end": 29}, "tail": {"text": "geometric representation", "start": 103, "end": 127}}, {"head": {"text": "ML MD methods", "start": 16, "end": 29}, "tail": {"text": "energy prediction", "start": 132, "end": 149}}, {"head": {"text": "automatic differential tools", "start": 214, "end": 242}, "tail": {"text": "trajectory learning", "start": 247, "end": 266}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Newtonian Dynamics and Langevin Dynamics .", "output": {"entities": {"method": [{"text": "Newtonian Dynamics", "start": 0, "end": 18}, {"text": "Langevin Dynamics .", "start": 23, "end": 42}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Newtonian dynamics is suitable for idealized systems with negligible thermal effects or when deterministic trajectories are required , while Langevin dynamics is adopted where thermal effects play a significant role and when the system is being studied at a finite temperature .", "output": {"entities": {"method": [{"text": "Newtonian dynamics", "start": 0, "end": 18}, {"text": "Langevin dynamics", "start": 141, "end": 158}]}, "relations": {"compare with": [{"head": {"text": "Newtonian dynamics", "start": 0, "end": 18}, "tail": {"text": "Langevin dynamics", "start": 141, "end": 158}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this work , we propose two versions : an ordinary differential equation ( ODE ) solver and a stochastic differential equation ( SDE ) solver concerning Newtonian dynamics and Langevin dynamics , respectively .", "output": {"entities": {"method": [{"text": "ordinary differential equation", "start": 44, "end": 74}, {"text": "ODE", "start": 77, "end": 80}, {"text": "stochastic differential equation", "start": 96, "end": 128}, {"text": "SDE", "start": 131, "end": 134}, {"text": "Newtonian dynamics", "start": 155, "end": 173}, {"text": "Langevin dynamics", "start": 178, "end": 195}]}, "relations": {"synonym of": [{"head": {"text": "ODE", "start": 77, "end": 80}, "tail": {"text": "ordinary differential equation", "start": 44, "end": 74}}, {"head": {"text": "SDE", "start": 131, "end": 134}, "tail": {"text": "stochastic differential equation", "start": 96, "end": 128}}], "part of": [{"head": {"text": "ordinary differential equation", "start": 44, "end": 74}, "tail": {"text": "Newtonian dynamics", "start": 155, "end": 173}}, {"head": {"text": "stochastic differential equation", "start": 96, "end": 128}, "tail": {"text": "Langevin dynamics", "start": 178, "end": 195}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Noticeably , our experiential dataset , MISATO [ 10 ] , uses Newtonian dynamics with Langevin thermostats , and the information on solvent molecules is not provided .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 40, "end": 46}], "method": [{"text": "Newtonian dynamics", "start": 61, "end": 79}, {"text": "Langevin thermostats", "start": 85, "end": 105}]}, "relations": {"evaluated with": [{"head": {"text": "Newtonian dynamics", "start": 61, "end": 79}, "tail": {"text": "MISATO", "start": 40, "end": 46}}], "part of": [{"head": {"text": "Langevin thermostats", "start": 85, "end": 105}, "tail": {"text": "Newtonian dynamics", "start": 61, "end": 79}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We also want to clarify two critical points about this problem setting : ( 1 ) Our task is trajectory prediction , i.e . , positions as labels , and no explicit energy and force are considered as labels .", "output": {"entities": {"task": [{"text": "trajectory prediction", "start": 91, "end": 112}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "ML methods for energy prediction followed with numerical ODE / SDE solver require smaller timestep ( e.g . , 1e - 15 seconds ) , while trajectory prediction , which directly predicts the coordinates over time , is agnostic to the magnitude of the timestep .", "output": {"entities": {"method": [{"text": "ML", "start": 0, "end": 2}, {"text": "ODE", "start": 57, "end": 60}, {"text": "SDE", "start": 63, "end": 66}], "task": [{"text": "energy prediction", "start": 15, "end": 32}, {"text": "trajectory prediction", "start": 135, "end": 156}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 0, "end": 2}, "tail": {"text": "energy prediction", "start": 15, "end": 32}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It has two main phases : ( 1 ) A multi-grained SE ( 3 ) - equivariant geometric model , BindingNet .", "output": {"entities": {"method": [{"text": "multi-grained SE ( 3 ) - equivariant geometric model", "start": 33, "end": 85}, {"text": "BindingNet", "start": 88, "end": 98}]}, "relations": {"subclass of": [{"head": {"text": "BindingNet", "start": 88, "end": 98}, "tail": {"text": "multi-grained SE ( 3 ) - equivariant geometric model", "start": 33, "end": 85}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It models the protein-ligand complex from three granularities , employing three levels of vector frame basis : atom level for ligands , backbone level for proteins , and residue level for protein-ligand complexes . ( 2 ) A second-order ordinary differential equation ( ODE ) solver and a second-order stochastic differential equation ( SDE ) to learn the Newtonian mechanics .", "output": {"entities": {"method": [{"text": "second-order ordinary differential equation", "start": 223, "end": 266}, {"text": "ODE", "start": 269, "end": 272}, {"text": "second-order stochastic differential equation", "start": 288, "end": 333}, {"text": "SDE", "start": 336, "end": 339}]}, "relations": {"synonym of": [{"head": {"text": "ODE", "start": 269, "end": 272}, "tail": {"text": "second-order ordinary differential equation", "start": 223, "end": 266}}, {"head": {"text": "SDE", "start": 336, "end": 339}, "tail": {"text": "second-order stochastic differential equation", "start": 288, "end": 333}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We further introduce two attributes : a derivative-space augmentation method for the efficient second-order solver and a conditional adjoint solver for lower memory costs .", "output": {"entities": {"method": [{"text": "derivative-space augmentation", "start": 40, "end": 69}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This section outlines the structure : the three levels of vector frames in Section 3.1 , the architecture of BindingNet for protein-ligand force prediction in Section 3.2 , the design of NeuralMD for trajectory simulation in Section 3.3 , and implementation details in Section 3.4 .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 109, "end": 119}, {"text": "NeuralMD", "start": 187, "end": 195}], "task": [{"text": "protein-ligand force prediction", "start": 124, "end": 155}, {"text": "trajectory simulation", "start": 200, "end": 221}]}, "relations": {"used for": [{"head": {"text": "BindingNet", "start": 109, "end": 119}, "tail": {"text": "protein-ligand force prediction", "start": 124, "end": 155}}, {"head": {"text": "NeuralMD", "start": 187, "end": 195}, "tail": {"text": "trajectory simulation", "start": 200, "end": 221}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To address this issue , we propose BindingNet , a multi-grained SE ( 3 ) - equivariant model , to capture the interactions between a ligand and a protein .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 35, "end": 45}, {"text": "multi-grained SE ( 3 ) - equivariant model", "start": 50, "end": 92}]}, "relations": {"subclass of": [{"head": {"text": "BindingNet", "start": 35, "end": 45}, "tail": {"text": "multi-grained SE ( 3 ) - equivariant model", "start": 50, "end": 92}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Such a group symmetric property is called SE ( 3 ) - equivariance .", "output": {"entities": {"method": [{"text": "SE ( 3 ) - equivariance", "start": 42, "end": 65}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Atom-Level Vector Frame for Ligands .", "output": {"entities": {"method": [{"text": "Atom-Level Vector Frame", "start": 0, "end": 23}], "task": [{"text": "Ligands", "start": 28, "end": 35}]}, "relations": {"used for": [{"head": {"text": "Atom-Level Vector Frame", "start": 0, "end": 23}, "tail": {"text": "Ligands", "start": 28, "end": 35}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For each residue in the protein , the coordinates are x x x N , x x x C α , and x x x C , then the backbone-level vector frame for this residue is : EQUATION This is built for each residue , enabling the message passing for a residue-level representation .", "output": {"entities": {"method": [{"text": "backbone-level vector frame", "start": 99, "end": 126}, {"text": "residue-level representation", "start": 226, "end": 254}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Residue-Level Vector Frame for Protein-Ligand Complexes .", "output": {"entities": {"method": [{"text": "Residue-Level Vector Frame", "start": 0, "end": 26}], "task": [{"text": "Protein-Ligand Complexes", "start": 31, "end": 55}]}, "relations": {"used for": [{"head": {"text": "Residue-Level Vector Frame", "start": 0, "end": 26}, "tail": {"text": "Protein-Ligand Complexes", "start": 31, "end": 55}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this section , we introduce BindingNet , a multi-grained SE ( 3 ) - equivariant geometric model for protein-ligand binding .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 31, "end": 41}, {"text": "multi-grained SE ( 3 ) - equivariant geometric model", "start": 46, "end": 98}], "task": [{"text": "protein-ligand binding", "start": 103, "end": 125}]}, "relations": {"subclass of": [{"head": {"text": "BindingNet", "start": 31, "end": 41}, "tail": {"text": "multi-grained SE ( 3 ) - equivariant geometric model", "start": 46, "end": 98}}], "used for": [{"head": {"text": "BindingNet", "start": 31, "end": 41}, "tail": {"text": "protein-ligand binding", "start": 103, "end": 125}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The input of BindingNet is the geometry of the rigid protein and the ligand at time t , while the output is the force on each atom in the ligand .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 13, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Atom-Level Ligand Modeling .", "output": {"entities": {"task": [{"text": "Atom-Level Ligand Modeling", "start": 0, "end": 26}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We first generate the atom embedding using one-hot encoding and then aggregate each atom's embedding , z z z ( l ) , by aggregating all its neighbor's embedding within the cutoff distance c .", "output": {"entities": {"method": [{"text": "atom embedding", "start": 22, "end": 36}, {"text": "one-hot encoding", "start": 43, "end": 59}]}, "relations": {"part of": [{"head": {"text": "one-hot encoding", "start": 43, "end": 59}, "tail": {"text": "atom embedding", "start": 22, "end": 36}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Finally , it is passed through several equivariant message-passing layers ( MPNN ) defined as : EQUATION where MLP ( • ) and Agg ( • ) are the multi-layer perceptron and mean aggregation functions , respectively . vec ∈ R 3 is a vector assigned to each atom and is initialized as 0 .", "output": {"entities": {"method": [{"text": "equivariant message-passing layers", "start": 39, "end": 73}, {"text": "MPNN", "start": 76, "end": 80}, {"text": "MLP", "start": 111, "end": 114}, {"text": "Agg", "start": 125, "end": 128}, {"text": "multi-layer perceptron", "start": 143, "end": 165}, {"text": "mean aggregation functions", "start": 170, "end": 196}]}, "relations": {"synonym of": [{"head": {"text": "MPNN", "start": 76, "end": 80}, "tail": {"text": "equivariant message-passing layers", "start": 39, "end": 73}}, {"head": {"text": "MLP", "start": 111, "end": 114}, "tail": {"text": "multi-layer perceptron", "start": 143, "end": 165}}, {"head": {"text": "Agg", "start": 125, "end": 128}, "tail": {"text": "mean aggregation functions", "start": 170, "end": 196}}], "part of": [{"head": {"text": "MLP", "start": 111, "end": 114}, "tail": {"text": "equivariant message-passing layers", "start": 39, "end": 73}}, {"head": {"text": "Agg", "start": 125, "end": 128}, "tail": {"text": "equivariant message-passing layers", "start": 39, "end": 73}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The outputs are atom representation and vector ( h h h ( l ) and vec ( l ) ) , and they are passed to the complex module introduced below .", "output": {"entities": {"method": [{"text": "atom representation", "start": 16, "end": 35}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Backbone-Level Protein Modeling .", "output": {"entities": {"task": [{"text": "Backbone-Level Protein Modeling", "start": 0, "end": 31}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For the coarse-grained modeling of proteins , we consider three backbone atoms in each residue .", "output": {"entities": {"task": [{"text": "coarse-grained modeling of proteins", "start": 8, "end": 43}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Then , we obtain an equivariant atom representation by aggregating the edge information , ( x x x ( p ) i - x x x ( p ) j ) • z z z ( p ) i , within cutoff distance c .", "output": {"entities": {"method": [{"text": "aggregating the edge information", "start": 55, "end": 87}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Finally , we adopt an equivariant MPNN layer to get the atom-level force as : EQUATION The ultimate force predictions for each atom include two parts : the internal force from the molecule vec ( l ) i and the external force from the protein-ligand interaction vec ( pl ) i j .", "output": {"entities": {"method": [{"text": "MPNN", "start": 34, "end": 38}], "task": [{"text": "protein-ligand interaction", "start": 233, "end": 259}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "So sum them up , we have : EQUATION These three modules consist BindingNet .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 64, "end": 74}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The BindingNet introduced in Sections 3.1 and 3.2 takes in the molecular system geometry at time t and outputs the forces .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 4, "end": 14}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Then in this section , we describe how we use the neural differential equation solver to predict the coordinates at future snapshots .", "output": {"entities": {"method": [{"text": "neural differential equation solver", "start": 50, "end": 85}], "task": [{"text": "predict the coordinates", "start": 89, "end": 112}]}, "relations": {"used for": [{"head": {"text": "neural differential equation solver", "start": 50, "end": 85}, "tail": {"text": "predict the coordinates", "start": 89, "end": 112}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We want to highlight that one ML for MD simulation research line is predicting the energy or force [ 36 , 23 , 9 ] , which will be fed into the numerical integration algorithms for trajectory simulation .", "output": {"entities": {"method": [{"text": "ML", "start": 30, "end": 32}, {"text": "numerical integration algorithms", "start": 144, "end": 176}], "task": [{"text": "MD simulation", "start": 37, "end": 50}, {"text": "trajectory simulation", "start": 181, "end": 202}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 30, "end": 32}, "tail": {"text": "MD simulation", "start": 37, "end": 50}}, {"head": {"text": "numerical integration algorithms", "start": 144, "end": 176}, "tail": {"text": "trajectory simulation", "start": 181, "end": 202}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For accuracy , such an ML-based MD simulation must be at the femtosecond level ( 1e - 15 second ) .", "output": {"entities": {"task": [{"text": "ML-based MD simulation", "start": 23, "end": 45}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , as shown in recent works [ 37 , 38 , 21 ] , minor errors in the ML force field can lead to catastrophic failure for long-time simulations .", "output": {"entities": {"method": [{"text": "ML", "start": 74, "end": 76}], "task": [{"text": "long-time simulations", "start": 126, "end": 147}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , however , we overcome this issue by directly learning the extended-timescale MD trajectories ( nanosecond level , 1e - 9 second ) .", "output": {"entities": {"task": [{"text": "extended-timescale MD trajectories", "start": 74, "end": 108}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Newtonian dynamics and Langevin dynamics for MD simulation .", "output": {"entities": {"method": [{"text": "Newtonian dynamics", "start": 0, "end": 18}, {"text": "Langevin dynamics", "start": 23, "end": 40}], "task": [{"text": "MD simulation", "start": 45, "end": 58}]}, "relations": {"used for": [{"head": {"text": "Langevin dynamics", "start": 23, "end": 40}, "tail": {"text": "MD simulation", "start": 45, "end": 58}}, {"head": {"text": "Newtonian dynamics", "start": 0, "end": 18}, "tail": {"text": "MD simulation", "start": 45, "end": 58}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For modeling , we consider using the BindingNet introduced above for force prediction at time τ , as : EQUATION On the other hand , Langevin dynamics introduces a stochastic component for large molecular systems with thermal fluctuations .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 37, "end": 47}, {"text": "Langevin dynamics", "start": 132, "end": 149}, {"text": "molecular systems", "start": 194, "end": 211}, {"text": "thermal fluctuations", "start": 217, "end": 237}], "task": [{"text": "force prediction", "start": 69, "end": 85}]}, "relations": {"used for": [{"head": {"text": "BindingNet", "start": 37, "end": 47}, "tail": {"text": "force prediction", "start": 69, "end": 85}}], "part of": [{"head": {"text": "thermal fluctuations", "start": 217, "end": 237}, "tail": {"text": "molecular systems", "start": 194, "end": 211}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Ultimately , adopting either F ( l ) τ - ODE or F ( l ) τ - SDE as the force prediction F ( l ) τ on each atom , the coordinates at time t can be obtained after integration as : EQUATION ) The training objective is the mean absolute error ( MAE ) between the predicted coordinates and ground-truth coordinates along the whole trajectories : EQUATION ) An illustration of NeuralMD pipeline is in Figure 4 .", "output": {"entities": {"method": [{"text": "ODE", "start": 41, "end": 44}, {"text": "SDE", "start": 60, "end": 63}, {"text": "mean absolute error", "start": 219, "end": 238}, {"text": "MAE", "start": 241, "end": 244}, {"text": "NeuralMD", "start": 371, "end": 379}], "task": [{"text": "force prediction", "start": 71, "end": 87}]}, "relations": {"used for": [{"head": {"text": "ODE", "start": 41, "end": 44}, "tail": {"text": "force prediction", "start": 71, "end": 87}}, {"head": {"text": "SDE", "start": 60, "end": 63}, "tail": {"text": "force prediction", "start": 71, "end": 87}}], "synonym of": [{"head": {"text": "MAE", "start": 241, "end": 244}, "tail": {"text": "mean absolute error", "start": 219, "end": 238}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Adjoint method .", "output": {"entities": {"method": [{"text": "Adjoint method", "start": 0, "end": 14}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Specifically for protein-ligand complexes with a large volume of atoms , the memory costs have become a main challenge .", "output": {"entities": {"task": [{"text": "protein-ligand complexes", "start": 17, "end": 41}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To handle this , we consider the adjoint method [ 28 ] , allowing small step sizes in the integration .", "output": {"entities": {"method": [{"text": "adjoint method", "start": 33, "end": 47}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Roughly speaking , the adjoint method calculates the variational derivative of the trajectory w.r.t . our objective function directly .", "output": {"entities": {"method": [{"text": "adjoint method", "start": 23, "end": 37}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Second-order differential equation .", "output": {"entities": {"method": [{"text": "Second-order differential equation", "start": 0, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The key module of the neural differential method is the differential function [ 28 ] , which returns the first-order derivative .", "output": {"entities": {"method": [{"text": "neural differential method", "start": 22, "end": 48}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To learn the MD trajectory following second-order ODE and SDE , we propose the following formulation of the second-order equation within one integration call : EQUATION ) We mark this as \" func \" in Figure 4 .", "output": {"entities": {"task": [{"text": "MD trajectory", "start": 13, "end": 26}], "method": [{"text": "ODE", "start": 50, "end": 53}, {"text": "SDE", "start": 58, "end": 61}]}, "relations": {"used for": [{"head": {"text": "ODE", "start": 50, "end": 53}, "tail": {"text": "MD trajectory", "start": 13, "end": 26}}, {"head": {"text": "SDE", "start": 58, "end": 61}, "tail": {"text": "MD trajectory", "start": 13, "end": 26}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This means we can augment ODE or SDE derivative space by concurrently calculating the accelerations and velocities , allowing simultaneous integration of velocities and positions .", "output": {"entities": {"method": [{"text": "ODE", "start": 26, "end": 29}, {"text": "SDE", "start": 33, "end": 36}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this section , we would like to include extra details of NeuralMD .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 60, "end": 68}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As in Equations ( 11 ) and ( 13 ) , both the coordinates and velocities are required inputs for NeuralMD .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 96, "end": 104}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To handle this issue , we propose a surrogate velocity , which is a summation of a predicted velocity by an extra equivariant model and a coordinate momentum , i.e . , v v v ( l ) t = BindingNet-Ligand ( f ( l ) , x x x ( l ) t ) + ( x x x ( l ) t + 1 - x x x ( l ) t ) , where the BindingNet-Ligand is the ligand model described in Section 3.2 .", "output": {"entities": {"method": [{"text": "coordinate momentum", "start": 138, "end": 157}, {"text": "BindingNet-Ligand", "start": 184, "end": 201}, {"text": "BindingNet-Ligand", "start": 282, "end": 299}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Update model BindingNet using gradient descent . 9 : end for 10 : end for 4 Experiments Datasets .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 13, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "One of the main bottlenecks of studying ML for molecular dynamics simulation in protein-ligand binding is insufficient data .", "output": {"entities": {"method": [{"text": "ML", "start": 40, "end": 42}], "task": [{"text": "molecular dynamics simulation", "start": 47, "end": 76}, {"text": "protein-ligand binding", "start": 80, "end": 102}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 40, "end": 42}, "tail": {"text": "molecular dynamics simulation", "start": 47, "end": 76}}], "subtask of": [{"head": {"text": "molecular dynamics simulation", "start": 47, "end": 76}, "tail": {"text": "protein-ligand binding", "start": 80, "end": 102}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Recently , the community has put more effort into gathering the datasets , and we consider MISATO in our work [ 10 ] .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 91, "end": 97}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It is built on 16,972 experimental protein-ligand complexes extracted from the protein data bank ( PDB ) [ 39 ] .", "output": {"entities": {"task": [{"text": "protein-ligand complexes", "start": 35, "end": 59}], "dataset": [{"text": "protein data bank", "start": 79, "end": 96}, {"text": "PDB", "start": 99, "end": 102}]}, "relations": {"benchmark for": [{"head": {"text": "protein data bank", "start": 79, "end": 96}, "tail": {"text": "protein-ligand complexes", "start": 35, "end": 59}}], "synonym of": [{"head": {"text": "PDB", "start": 99, "end": 102}, "tail": {"text": "protein data bank", "start": 79, "end": 96}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Such data is obtained using X-ray crystallography , Nuclear Magnetic Resonance ( NMR ) , or Cryo-Electron Microscopy ( Cryo-EM ) , where systematic errors are unavoidable .", "output": {"entities": {"method": [{"text": "X-ray crystallography", "start": 28, "end": 49}, {"text": "Nuclear Magnetic Resonance", "start": 52, "end": 78}, {"text": "NMR", "start": 81, "end": 84}, {"text": "Cryo-Electron Microscopy", "start": 92, "end": 116}, {"text": "Cryo-EM", "start": 119, "end": 126}]}, "relations": {"synonym of": [{"head": {"text": "NMR", "start": 81, "end": 84}, "tail": {"text": "Nuclear Magnetic Resonance", "start": 52, "end": 78}}, {"head": {"text": "Cryo-EM", "start": 119, "end": 126}, "tail": {"text": "Cryo-Electron Microscopy", "start": 92, "end": 116}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For each protein-ligand complex , the trajectory comprises 100 snapshots in 8 nanoseconds under the fixed temperature and pressure .", "output": {"entities": {"task": [{"text": "protein-ligand complex", "start": 9, "end": 31}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In Appendix E , we list the basic statistics of MISATO , e.g . , the number of atoms in small molecule ligands , and the number of residues in proteins .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 48, "end": 54}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Using ML for energy and force prediction , followed by trajectory prediction using numerical integration method , has been widely explored in the community , e.g . , HDNNPs [ 7 ] , DeePMD [ 9 ] , TorchMD [ 36 ] , and Allegro-LAMMPS [ 23 ] .", "output": {"entities": {"method": [{"text": "ML", "start": 6, "end": 8}, {"text": "numerical integration method", "start": 83, "end": 111}, {"text": "HDNNPs", "start": 166, "end": 172}, {"text": "DeePMD", "start": 181, "end": 187}, {"text": "TorchMD", "start": 196, "end": 203}, {"text": "Allegro-LAMMPS", "start": 217, "end": 231}], "task": [{"text": "force prediction", "start": 24, "end": 40}, {"text": "trajectory prediction", "start": 55, "end": 76}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 6, "end": 8}, "tail": {"text": "force prediction", "start": 24, "end": 40}}, {"head": {"text": "numerical integration method", "start": 83, "end": 111}, "tail": {"text": "trajectory prediction", "start": 55, "end": 76}}, {"head": {"text": "HDNNPs", "start": 166, "end": 172}, "tail": {"text": "trajectory prediction", "start": 55, "end": 76}}, {"head": {"text": "DeePMD", "start": 181, "end": 187}, "tail": {"text": "trajectory prediction", "start": 55, "end": 76}}, {"head": {"text": "TorchMD", "start": 196, "end": 203}, "tail": {"text": "trajectory prediction", "start": 55, "end": 76}}, {"head": {"text": "Allegro-LAMMPS", "start": 217, "end": 231}, "tail": {"text": "trajectory prediction", "start": 55, "end": 76}}], "subclass of": [{"head": {"text": "HDNNPs", "start": 166, "end": 172}, "tail": {"text": "numerical integration method", "start": 83, "end": 111}}, {"head": {"text": "DeePMD", "start": 181, "end": 187}, "tail": {"text": "numerical integration method", "start": 83, "end": 111}}, {"head": {"text": "TorchMD", "start": 196, "end": 203}, "tail": {"text": "numerical integration method", "start": 83, "end": 111}}, {"head": {"text": "Allegro-LAMMPS", "start": 217, "end": 231}, "tail": {"text": "numerical integration method", "start": 83, "end": 111}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here we extend this paradigm for binding dynamics and propose VerletMD , which utilizes BindingNet for energy prediction on each snapshot and velocity Verlet algorithm to get the trajectory .", "output": {"entities": {"method": [{"text": "VerletMD", "start": 62, "end": 70}, {"text": "BindingNet", "start": 88, "end": 98}, {"text": "velocity Verlet algorithm", "start": 142, "end": 167}], "task": [{"text": "energy prediction", "start": 103, "end": 120}]}, "relations": {"part of": [{"head": {"text": "BindingNet", "start": 88, "end": 98}, "tail": {"text": "VerletMD", "start": 62, "end": 70}}, {"head": {"text": "velocity Verlet algorithm", "start": 142, "end": 167}, "tail": {"text": "VerletMD", "start": 62, "end": 70}}], "used for": [{"head": {"text": "BindingNet", "start": 88, "end": 98}, "tail": {"text": "energy prediction", "start": 103, "end": 120}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Additionally , we mainly focus on ML methods for trajectory prediction in this work , i.e . , no energy or force is considered as labels .", "output": {"entities": {"method": [{"text": "ML", "start": 34, "end": 36}], "task": [{"text": "trajectory prediction", "start": 49, "end": 70}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 34, "end": 36}, "tail": {"text": "trajectory prediction", "start": 49, "end": 70}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "GNN-MD is to apply geometric graph neural networks ( GNNs ) to predict the trajectories in an auto-regressive manner [ 10 , 24 ] .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 0, "end": 6}, {"text": "graph neural networks", "start": 29, "end": 50}, {"text": "GNNs", "start": 53, "end": 57}], "task": [{"text": "predict the trajectories", "start": 63, "end": 87}]}, "relations": {"part of": [{"head": {"text": "graph neural networks", "start": 29, "end": 50}, "tail": {"text": "GNN-MD", "start": 0, "end": 6}}], "synonym of": [{"head": {"text": "GNNs", "start": 53, "end": 57}, "tail": {"text": "graph neural networks", "start": 29, "end": 50}}], "used for": [{"head": {"text": "GNN-MD", "start": 0, "end": 6}, "tail": {"text": "predict the trajectories", "start": 63, "end": 87}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "More concretely , GNN-MD takes the coordinates and other molecular information as inputs at time t and predicts the coordinates at time t + 1 .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 18, "end": 24}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "DenoisingLD ( denoising diffusion for Langevin dynamics ) [ 24 , 25 , 26 ] is a baseline method that models the trajectory prediction as denoising diffusion task [ 40 ] , and the inference for binding trajectory essentially becomes the Langevin dynamics .", "output": {"entities": {"method": [{"text": "DenoisingLD", "start": 0, "end": 11}, {"text": "denoising diffusion for Langevin dynamics", "start": 14, "end": 55}], "task": [{"text": "trajectory prediction", "start": 112, "end": 133}, {"text": "denoising diffusion task", "start": 137, "end": 161}, {"text": "binding trajectory", "start": 193, "end": 211}]}, "relations": {"synonym of": [{"head": {"text": "DenoisingLD", "start": 0, "end": 11}, "tail": {"text": "denoising diffusion for Langevin dynamics", "start": 14, "end": 55}}], "used for": [{"head": {"text": "DenoisingLD", "start": 0, "end": 11}, "tail": {"text": "trajectory prediction", "start": 112, "end": 133}}, {"head": {"text": "DenoisingLD", "start": 0, "end": 11}, "tail": {"text": "denoising diffusion task", "start": 137, "end": 161}}, {"head": {"text": "DenoisingLD", "start": 0, "end": 11}, "tail": {"text": "binding trajectory", "start": 193, "end": 211}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , to make the comparison more explicit , we compare these two methods ( GNN-MD and DenoisingLD ) separately .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 77, "end": 83}, {"text": "DenoisingLD", "start": 88, "end": 99}]}, "relations": {"compare with": [{"head": {"text": "GNN-MD", "start": 77, "end": 83}, "tail": {"text": "DenoisingLD", "start": 88, "end": 99}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We want to highlight that we keep the same backbone model , BindingNet , for energy or force prediction for all the baselines and NeuralMD .", "output": {"entities": {"method": [{"text": "BindingNet", "start": 60, "end": 70}, {"text": "NeuralMD", "start": 130, "end": 138}], "task": [{"text": "force prediction", "start": 87, "end": 103}]}, "relations": {"used for": [{"head": {"text": "NeuralMD", "start": 130, "end": 138}, "tail": {"text": "force prediction", "start": 87, "end": 103}}, {"head": {"text": "BindingNet", "start": 60, "end": 70}, "tail": {"text": "force prediction", "start": 87, "end": 103}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To evaluate this , we take both the mean absolute error ( MAE ) and mean squared error ( MSE ) between the predicted coordinates and ground-truth coordinates over all snapshots .", "output": {"entities": {"method": [{"text": "mean absolute error", "start": 36, "end": 55}, {"text": "MAE", "start": 58, "end": 61}, {"text": "mean squared error", "start": 68, "end": 86}, {"text": "MSE", "start": 89, "end": 92}]}, "relations": {"synonym of": [{"head": {"text": "MAE", "start": 58, "end": 61}, "tail": {"text": "mean absolute error", "start": 36, "end": 55}}, {"head": {"text": "MSE", "start": 89, "end": 92}, "tail": {"text": "mean squared error", "start": 68, "end": 86}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Stability , as highlighted in [ 21 ] , is an important metric for evaluating the predicted MD trajectory .", "output": {"entities": {"task": [{"text": "predicted MD trajectory", "start": 81, "end": 104}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The intuition is that the prediction on long-time MD trajectory can enter a pathological state ( e.g . , bond breaking ) , and stability is the measure to quantify such observation .", "output": {"entities": {"task": [{"text": "long-time MD trajectory", "start": 40, "end": 63}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Another metric considered is frames per second ( FPS ) [ 21 ] on a single Nvidia-V100 GPU card , and it measures the MD efficiency .", "output": {"entities": {"method": [{"text": "MD", "start": 117, "end": 119}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The first observation is that the baseline VertletMD has a clear performance gap compared to the other methods .", "output": {"entities": {"method": [{"text": "VertletMD", "start": 43, "end": 52}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "There are two possible reasons : ( 1 ) Using ML models to predict the energy ( or force ) at each snapshot , and then using a numerical integration algorithm can fail in the long-time simulations [ 21 ] ; ( 2 ) ML for energy prediction methods require more data to train than the ML for coordinate prediction methods , thus they can perform worse in the low-data regime here .", "output": {"entities": {"method": [{"text": "ML", "start": 45, "end": 47}, {"text": "numerical integration algorithm", "start": 126, "end": 157}, {"text": "ML", "start": 211, "end": 213}, {"text": "ML", "start": 280, "end": 282}], "task": [{"text": "predict the energy", "start": 58, "end": 76}, {"text": "long-time simulations", "start": 174, "end": 195}, {"text": "energy prediction", "start": 218, "end": 235}, {"text": "coordinate prediction", "start": 287, "end": 308}]}, "relations": {"used for": [{"head": {"text": "ML", "start": 45, "end": 47}, "tail": {"text": "predict the energy", "start": 58, "end": 76}}, {"head": {"text": "numerical integration algorithm", "start": 126, "end": 157}, "tail": {"text": "long-time simulations", "start": 174, "end": 195}}, {"head": {"text": "ML", "start": 211, "end": 213}, "tail": {"text": "energy prediction", "start": 218, "end": 235}}, {"head": {"text": "ML", "start": 280, "end": 282}, "tail": {"text": "coordinate prediction", "start": 287, "end": 308}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , the stability ( % ) can be a distinctive factor in method comparisons , where we observe the two variants of NeuralMD outperform on all 10 tasks up to ~ 80% .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 119, "end": 127}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It is shown that the GNN-MD collapses occasionally , while DenoisingLD stays comparatively structured .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 21, "end": 27}, {"text": "DenoisingLD", "start": 59, "end": 70}]}, "relations": {"compare with": [{"head": {"text": "GNN-MD", "start": 21, "end": 27}, "tail": {"text": "DenoisingLD", "start": 59, "end": 70}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Meanwhile , NeuralMD is the most stable in all cases .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 12, "end": 20}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "One main benefit of using NeuralMD for binding simulation is its efficiency .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 26, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We further approximate the wall time of the numerical method for MD simulation ( PDB 5WIJ ) .", "output": {"entities": {"method": [{"text": "numerical method", "start": 44, "end": 60}], "task": [{"text": "MD simulation", "start": 65, "end": 78}], "dataset": [{"text": "PDB 5WIJ", "start": 81, "end": 89}]}, "relations": {"used for": [{"head": {"text": "numerical method", "start": 44, "end": 60}, "tail": {"text": "MD simulation", "start": 65, "end": 78}}], "benchmark for": [{"head": {"text": "PDB 5WIJ", "start": 81, "end": 89}, "tail": {"text": "MD simulation", "start": 65, "end": 78}}], "evaluated with": [{"head": {"text": "numerical method", "start": 44, "end": 60}, "tail": {"text": "PDB 5WIJ", "start": 81, "end": 89}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A more challenging task is to test the generalization ability of NeuralMD among different trajectories .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 65, "end": 73}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The MISATO dataset includes 13,765 protein-ligand complexes , and we first create two small datasets by randomly sampling 100 and 1k complexes , respectively .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 4, "end": 10}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We also consider the whole MISATO dataset , where the data split has already been provided .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 27, "end": 33}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "First , we can observe that VerletMD has worse performance on all three datasets , and the performance gap with other methods is even larger compared to the single-trajectory prediction .", "output": {"entities": {"method": [{"text": "VerletMD", "start": 28, "end": 36}], "task": [{"text": "single-trajectory prediction", "start": 157, "end": 185}]}, "relations": {"used for": [{"head": {"text": "VerletMD", "start": 28, "end": 36}, "tail": {"text": "single-trajectory prediction", "start": 157, "end": 185}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The other two baselines , GNN-MD and DenoisingLD , show similar performance , while NeuralMD outperforms in all datasets .", "output": {"entities": {"method": [{"text": "GNN-MD", "start": 26, "end": 32}, {"text": "DenoisingLD", "start": 37, "end": 48}, {"text": "NeuralMD", "start": 84, "end": 92}]}, "relations": {"compare with": [{"head": {"text": "NeuralMD", "start": 84, "end": 92}, "tail": {"text": "GNN-MD", "start": 26, "end": 32}}, {"head": {"text": "NeuralMD", "start": 84, "end": 92}, "tail": {"text": "DenoisingLD", "start": 37, "end": 48}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Notice that stability ( % ) remains more distinguishable than the two trajectory recovery metrics ( MAE and MSE ) .", "output": {"entities": {"method": [{"text": "MAE", "start": 100, "end": 103}, {"text": "MSE", "start": 108, "end": 111}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To sum up , we devise NeuralMD , an ML framework that incorporates a novel multi-grained group symmetric network architecture and second-order Newtonian mechanics , enabling accurate predictions of binding dynamics in a large timescale .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 22, "end": 30}, {"text": "ML", "start": 36, "end": 38}, {"text": "multi-grained group symmetric network", "start": 75, "end": 112}], "task": [{"text": "binding dynamics", "start": 198, "end": 214}]}, "relations": {"part of": [{"head": {"text": "multi-grained group symmetric network", "start": 75, "end": 112}, "tail": {"text": "NeuralMD", "start": 22, "end": 30}}], "subclass of": [{"head": {"text": "NeuralMD", "start": 22, "end": 30}, "tail": {"text": "ML", "start": 36, "end": 38}}], "used for": [{"head": {"text": "NeuralMD", "start": 22, "end": 30}, "tail": {"text": "binding dynamics", "start": 198, "end": 214}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Not only is such a timescale critical for understanding the dynamic nature of the ligand-protein complex , but our work marks the first approach in developing a framework to predict coordinates for MD simulation in protein-ligand binding .", "output": {"entities": {"task": [{"text": "MD simulation", "start": 198, "end": 211}, {"text": "protein-ligand binding", "start": 215, "end": 237}]}, "relations": {"subtask of": [{"head": {"text": "MD simulation", "start": 198, "end": 211}, "tail": {"text": "protein-ligand binding", "start": 215, "end": 237}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We quantitatively and qualitatively verify that NeuralMD achieves superior performance on 13 binding dynamics tasks .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 48, "end": 56}], "task": [{"text": "binding dynamics", "start": 93, "end": 109}]}, "relations": {"used for": [{"head": {"text": "NeuralMD", "start": 48, "end": 56}, "tail": {"text": "binding dynamics", "start": 93, "end": 109}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Currently , we are using the MISATO dataset , a protein-ligand dynamics dataset with a large timestep .", "output": {"entities": {"dataset": [{"text": "MISATO", "start": 29, "end": 35}], "task": [{"text": "protein-ligand dynamics", "start": 48, "end": 71}]}, "relations": {"benchmark for": [{"head": {"text": "MISATO", "start": 29, "end": 35}, "tail": {"text": "protein-ligand dynamics", "start": 48, "end": 71}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , NeuralMD is agnostic to the timestep , and it can also be applied to binding dynamics datasets with timestep as a femtosecond .", "output": {"entities": {"method": [{"text": "NeuralMD", "start": 10, "end": 18}], "task": [{"text": "binding dynamics", "start": 79, "end": 95}]}, "relations": {"used for": [{"head": {"text": "NeuralMD", "start": 10, "end": 18}, "tail": {"text": "binding dynamics", "start": 79, "end": 95}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "3D Registration with Maximal Cliques As a fundamental problem in computer vision , 3D point cloud registration ( PCR ) aims to seek the optimal pose to align a point cloud pair .", "output": {"entities": {"task": [{"text": "3D Registration", "start": 0, "end": 15}, {"text": "computer vision", "start": 65, "end": 80}, {"text": "3D point cloud registration", "start": 83, "end": 110}, {"text": "PCR", "start": 113, "end": 116}], "method": [{"text": "Maximal Cliques", "start": 21, "end": 36}]}, "relations": {"used for": [{"head": {"text": "Maximal Cliques", "start": 21, "end": 36}, "tail": {"text": "3D Registration", "start": 0, "end": 15}}], "subtask of": [{"head": {"text": "3D Registration", "start": 0, "end": 15}, "tail": {"text": "computer vision", "start": 65, "end": 80}}, {"head": {"text": "3D point cloud registration", "start": 83, "end": 110}, "tail": {"text": "computer vision", "start": 65, "end": 80}}], "synonym of": [{"head": {"text": "PCR", "start": 113, "end": 116}, "tail": {"text": "3D point cloud registration", "start": 83, "end": 110}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we present a 3D registration method with maximal cliques ( MAC ) .", "output": {"entities": {"method": [{"text": "3D registration method", "start": 29, "end": 51}, {"text": "maximal cliques", "start": 57, "end": 72}, {"text": "MAC", "start": 75, "end": 78}]}, "relations": {"part of": [{"head": {"text": "maximal cliques", "start": 57, "end": 72}, "tail": {"text": "3D registration method", "start": 29, "end": 51}}], "synonym of": [{"head": {"text": "MAC", "start": 75, "end": 78}, "tail": {"text": "maximal cliques", "start": 57, "end": 72}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The key insight is to loosen the previous maximum clique constraint , and mine more local consensus information in a graph for accurate pose hypotheses generation : 1 ) A compatibility graph is constructed to render the affinity relationship between initial correspondences . 2 ) We search for maximal cliques in the graph , each of which represents a consensus set .", "output": {"entities": {"method": [{"text": "maximum clique", "start": 42, "end": 56}, {"text": "maximal cliques", "start": 294, "end": 309}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We perform node-guided clique selection then , where each node corresponds to the maximal clique with the greatest graph weight . 3 ) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration .", "output": {"entities": {"method": [{"text": "node-guided clique selection", "start": 11, "end": 39}, {"text": "maximal clique", "start": 82, "end": 96}, {"text": "SVD", "start": 205, "end": 208}], "task": [{"text": "registration", "start": 262, "end": 274}]}, "relations": {"used for": [{"head": {"text": "SVD", "start": 205, "end": 208}, "tail": {"text": "registration", "start": 262, "end": 274}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Extensive experiments on U3M , 3DMatch , 3DLoMatch and KITTI demonstrate that MAC effectively increases registration accuracy , outperforms various state-of-the-art methods and boosts the performance of deep-learned methods .", "output": {"entities": {"dataset": [{"text": "U3M", "start": 25, "end": 28}, {"text": "3DMatch", "start": 31, "end": 38}, {"text": "3DLoMatch", "start": 41, "end": 50}, {"text": "KITTI", "start": 55, "end": 60}], "method": [{"text": "MAC", "start": 78, "end": 81}, {"text": "deep-learned methods", "start": 203, "end": 223}], "task": [{"text": "registration", "start": 104, "end": 116}]}, "relations": {"evaluated with": [{"head": {"text": "MAC", "start": 78, "end": 81}, "tail": {"text": "U3M", "start": 25, "end": 28}}, {"head": {"text": "MAC", "start": 78, "end": 81}, "tail": {"text": "3DMatch", "start": 31, "end": 38}}, {"head": {"text": "MAC", "start": 78, "end": 81}, "tail": {"text": "3DLoMatch", "start": 41, "end": 50}}], "used for": [{"head": {"text": "MAC", "start": 78, "end": 81}, "tail": {"text": "registration", "start": 104, "end": 116}}], "benchmark for": [{"head": {"text": "U3M", "start": 25, "end": 28}, "tail": {"text": "registration", "start": 104, "end": 116}}, {"head": {"text": "3DMatch", "start": 31, "end": 38}, "tail": {"text": "registration", "start": 104, "end": 116}}, {"head": {"text": "3DLoMatch", "start": 41, "end": 50}, "tail": {"text": "registration", "start": 104, "end": 116}}, {"head": {"text": "KITTI", "start": 55, "end": 60}, "tail": {"text": "registration", "start": 104, "end": 116}}], "part of": [{"head": {"text": "MAC", "start": 78, "end": 81}, "tail": {"text": "deep-learned methods", "start": 203, "end": 223}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC combined with deep-learned methods achieves stateof-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}, {"text": "deep-learned methods", "start": 18, "end": 38}], "task": [{"text": "registration", "start": 64, "end": 76}], "dataset": [{"text": "3DMatch", "start": 104, "end": 111}, {"text": "3DLoMatch", "start": 114, "end": 123}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 0, "end": 3}, "tail": {"text": "deep-learned methods", "start": 18, "end": 38}}], "used for": [{"head": {"text": "deep-learned methods", "start": 18, "end": 38}, "tail": {"text": "registration", "start": 64, "end": 76}}], "benchmark for": [{"head": {"text": "3DLoMatch", "start": 114, "end": 123}, "tail": {"text": "registration", "start": 64, "end": 76}}, {"head": {"text": "3DMatch", "start": 104, "end": 111}, "tail": {"text": "registration", "start": 64, "end": 76}}], "evaluated with": [{"head": {"text": "deep-learned methods", "start": 18, "end": 38}, "tail": {"text": "3DMatch", "start": 104, "end": 111}}, {"head": {"text": "deep-learned methods", "start": 18, "end": 38}, "tail": {"text": "3DLoMatch", "start": 114, "end": 123}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Given two 3D scans of the same object ( or scene ) , the goal of PCR is to estimate a six-degree-of-freedom ( 6 - DoF ) pose transformation that accurately aligns the two input point clouds .", "output": {"entities": {"task": [{"text": "PCR", "start": 65, "end": 68}, {"text": "six-degree-of-freedom", "start": 86, "end": 107}, {"text": "6 - DoF", "start": 110, "end": 117}]}, "relations": {"synonym of": [{"head": {"text": "6 - DoF", "start": 110, "end": 117}, "tail": {"text": "six-degree-of-freedom", "start": 86, "end": 107}}], "used for": [{"head": {"text": "PCR", "start": 65, "end": 68}, "tail": {"text": "six-degree-of-freedom", "start": 86, "end": 107}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Using pointto-point feature correspondences is a popular and robust solution to the PCR problem .", "output": {"entities": {"task": [{"text": "PCR", "start": 84, "end": 87}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The problem of 3D registration by handling correspondences with outliers has been studied for decades .", "output": {"entities": {"task": [{"text": "3D registration", "start": 15, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We classify them into geometric-only and deep-learned methods .", "output": {"entities": {"method": [{"text": "geometric-only", "start": 22, "end": 36}, {"text": "deep-learned methods", "start": 41, "end": 61}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For geometric-only methods [ 5 , 6 , 21 , 30 , 31 , [ 38 ] [ 39 ] [ 40 ] [ 41 ] , random sample consensus ( RANSAC ) and its variants perform an iterative sampling strategy for registration .", "output": {"entities": {"method": [{"text": "geometric-only methods", "start": 4, "end": 26}, {"text": "random sample consensus", "start": 82, "end": 105}, {"text": "RANSAC", "start": 108, "end": 114}], "task": [{"text": "registration", "start": 177, "end": 189}]}, "relations": {"subclass of": [{"head": {"text": "random sample consensus", "start": 82, "end": 105}, "tail": {"text": "geometric-only methods", "start": 4, "end": 26}}], "synonym of": [{"head": {"text": "RANSAC", "start": 108, "end": 114}, "tail": {"text": "random sample consensus", "start": 82, "end": 105}}], "used for": [{"head": {"text": "random sample consensus", "start": 82, "end": 105}, "tail": {"text": "registration", "start": 177, "end": 189}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Although RANSAC-based methods are simple and efficient , their performance is highly vulnerable when the outlier rate increases , and it requires a large number of iterations to obtain acceptable results .", "output": {"entities": {"method": [{"text": "RANSAC-based methods", "start": 9, "end": 29}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Also , a series of global registration methods based on branch-and-bound ( BnB ) are proposed to search the 6D parameter space and obtain the optimal global solution .", "output": {"entities": {"method": [{"text": "global registration methods", "start": 19, "end": 46}, {"text": "branch-and-bound", "start": 56, "end": 72}, {"text": "BnB", "start": 75, "end": 78}]}, "relations": {"part of": [{"head": {"text": "branch-and-bound", "start": 56, "end": 72}, "tail": {"text": "global registration methods", "start": 19, "end": 46}}], "synonym of": [{"head": {"text": "BnB", "start": 75, "end": 78}, "tail": {"text": "branch-and-bound", "start": 56, "end": 72}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Except for this watermark , it is identical to the accepted version ; the final published version of the proceedings is available on IEEE Xplore . one module in the registration process , such as investigating more discriminate keypoint feature descriptors or more effective correspondence selection techniques , while the others [ 22 , 29 , 43 ] focus on registration in an end-to-end manner .", "output": {"entities": {"task": [{"text": "registration", "start": 165, "end": 177}, {"text": "registration", "start": 356, "end": 368}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , deep-learned based methods require a large amount of data for training and usually lack generalization on different datasets .", "output": {"entities": {"method": [{"text": "deep-learned based methods", "start": 10, "end": 36}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we propose a geometric-only 3D registration method based on maximal cliques ( MAC ) .", "output": {"entities": {"method": [{"text": "geometric-only 3D registration method", "start": 29, "end": 66}, {"text": "maximal cliques", "start": 76, "end": 91}, {"text": "MAC", "start": 94, "end": 97}]}, "relations": {"part of": [{"head": {"text": "maximal cliques", "start": 76, "end": 91}, "tail": {"text": "geometric-only 3D registration method", "start": 29, "end": 66}}], "synonym of": [{"head": {"text": "MAC", "start": 94, "end": 97}, "tail": {"text": "maximal cliques", "start": 76, "end": 91}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The key insight is to loosen the previous maximum clique constraint , and mine more local consensus information in a graph to generate accurate pose hypotheses .", "output": {"entities": {"method": [{"text": "maximum clique constraint", "start": 42, "end": 67}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Compared with the maximum clique , MAC is a looser constraint and is able to mine more local information in a graph .", "output": {"entities": {"method": [{"text": "maximum clique", "start": 18, "end": 32}, {"text": "MAC", "start": 35, "end": 38}]}, "relations": {"compare with": [{"head": {"text": "MAC", "start": 35, "end": 38}, "tail": {"text": "maximum clique", "start": 18, "end": 32}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Finally , transformation hypotheses are computed for the selected cliques by the SVD algorithm .", "output": {"entities": {"method": [{"text": "SVD", "start": 81, "end": 84}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The best hypothesis is selected to perform registration using popular hypothesis evaluation metrics in the RANSAC family .", "output": {"entities": {"task": [{"text": "registration", "start": 43, "end": 55}], "method": [{"text": "RANSAC", "start": 107, "end": 113}]}, "relations": {"used for": [{"head": {"text": "RANSAC", "start": 107, "end": 113}, "tail": {"text": "registration", "start": 43, "end": 55}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To summarize , our main contributions are as follows : • We introduce a hypothesis generation method named MAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 107, "end": 110}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our MAC method is able to mine more local information in a graph , compared with the previous maximum clique constraint .", "output": {"entities": {"method": [{"text": "MAC", "start": 4, "end": 7}, {"text": "maximum clique", "start": 94, "end": 108}]}, "relations": {"compare with": [{"head": {"text": "MAC", "start": 4, "end": 7}, "tail": {"text": "maximum clique", "start": 94, "end": 108}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Notably , our geometric-only MAC method outperforms several state-of-the-art deep learning methods [ 3 , 9 , 19 , 27 ] .", "output": {"entities": {"method": [{"text": "geometric-only MAC method", "start": 14, "end": 39}, {"text": "deep learning methods", "start": 77, "end": 98}]}, "relations": {"compare with": [{"head": {"text": "geometric-only MAC method", "start": 14, "end": 39}, "tail": {"text": "deep learning methods", "start": 77, "end": 98}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC can also be inserted as a module into multiple deep-learned frameworks [ 1 , 10 , 18 , 29 , 43 ] to boost their performance .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}, {"text": "deep-learned frameworks", "start": 51, "end": 74}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 0, "end": 3}, "tail": {"text": "deep-learned frameworks", "start": 51, "end": 74}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC combined with GeoTransformer achieves the state-of-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}, {"text": "GeoTransformer", "start": 18, "end": 32}], "task": [{"text": "registration", "start": 63, "end": 75}], "dataset": [{"text": "3DMatch", "start": 103, "end": 110}, {"text": "3DLoMatch", "start": 113, "end": 122}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 0, "end": 3}, "tail": {"text": "GeoTransformer", "start": 18, "end": 32}}], "used for": [{"head": {"text": "GeoTransformer", "start": 18, "end": 32}, "tail": {"text": "registration", "start": 63, "end": 75}}], "benchmark for": [{"head": {"text": "3DMatch", "start": 103, "end": 110}, "tail": {"text": "registration", "start": 63, "end": 75}}, {"head": {"text": "3DLoMatch", "start": 113, "end": 122}, "tail": {"text": "registration", "start": 63, "end": 75}}], "evaluated with": [{"head": {"text": "GeoTransformer", "start": 18, "end": 32}, "tail": {"text": "3DMatch", "start": 103, "end": 110}}, {"head": {"text": "GeoTransformer", "start": 18, "end": 32}, "tail": {"text": "3DLoMatch", "start": 113, "end": 122}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Various geometric-only methods [ 6 , 8 , 20 , 36 , 45 ] have been proposed recently .", "output": {"entities": {"method": [{"text": "geometric-only methods", "start": 8, "end": 30}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Typically , RANSAC and its variants [ 5 , 13 , 30 , 31 , [ 38 ] [ 39 ] [ 40 ] remain the dominant approaches to the problem of estimating a 6 - DoF pose from correspondences .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 12, "end": 18}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "RANSAC iteratively samples correspondences from the initial set , generating and evaluating geometric estimations for each subset until a satisfactory solution is obtained .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 0, "end": 6}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Efficient and robust evaluation metrics are extremely important for using RANSAC to achieve accurate registration .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 74, "end": 80}], "task": [{"text": "registration", "start": 101, "end": 113}]}, "relations": {"used for": [{"head": {"text": "RANSAC", "start": 74, "end": 80}, "tail": {"text": "registration", "start": 101, "end": 113}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To address the current problems of timeconsuming and noise-sensitive evaluation metrics , [ 40 ] analyzes the contribution of inliers and outliers during the computation and proposed several metrics that can effectively improve the registration performance of RANSAC .", "output": {"entities": {"task": [{"text": "registration", "start": 232, "end": 244}], "method": [{"text": "RANSAC", "start": 260, "end": 266}]}, "relations": {"used for": [{"head": {"text": "RANSAC", "start": 260, "end": 266}, "tail": {"text": "registration", "start": 232, "end": 244}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For example , Rusu et al . [ 31 ] presented the simple consensus-based initial alignment ( SAC-IA ) method , which samples correspondences spread out on the point cloud and leverages the Huber penalty for evaluation .", "output": {"entities": {"method": [{"text": "consensus-based initial alignment", "start": 55, "end": 88}, {"text": "SAC-IA", "start": 91, "end": 97}]}, "relations": {"synonym of": [{"head": {"text": "SAC-IA", "start": 91, "end": 97}, "tail": {"text": "consensus-based initial alignment", "start": 55, "end": 88}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Graph cut RANSAC ( GC-RANSAC ) [ 5 ] uses the graph-cut algorithm before model re-fitting in the local optimization step .", "output": {"entities": {"method": [{"text": "Graph cut RANSAC", "start": 0, "end": 16}, {"text": "GC-RANSAC", "start": 19, "end": 28}, {"text": "graph-cut", "start": 46, "end": 55}]}, "relations": {"synonym of": [{"head": {"text": "GC-RANSAC", "start": 19, "end": 28}, "tail": {"text": "Graph cut RANSAC", "start": 0, "end": 16}}], "part of": [{"head": {"text": "graph-cut", "start": 46, "end": 55}, "tail": {"text": "Graph cut RANSAC", "start": 0, "end": 16}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Compatibility-guided sample consensus ( CG-SAC ) [ 30 ] additionally considers the normal information of key points during the sampling process .", "output": {"entities": {"method": [{"text": "Compatibility-guided sample consensus", "start": 0, "end": 37}, {"text": "CG-SAC", "start": 40, "end": 46}]}, "relations": {"synonym of": [{"head": {"text": "CG-SAC", "start": 40, "end": 46}, "tail": {"text": "Compatibility-guided sample consensus", "start": 0, "end": 37}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Yang et al . [ 39 ] proposed the sample consensus by sampling compatibility triangles ( SAC-COT ) method , which generates estimations by ranking and sampling ternary loops from the compatibility graph .", "output": {"entities": {"method": [{"text": "sampling compatibility triangles", "start": 53, "end": 85}, {"text": "SAC-COT", "start": 88, "end": 95}]}, "relations": {"synonym of": [{"head": {"text": "SAC-COT", "start": 88, "end": 95}, "tail": {"text": "sampling compatibility triangles", "start": 53, "end": 85}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A series of globally optimal methods based on BnB have been proposed recently .", "output": {"entities": {"method": [{"text": "BnB", "start": 46, "end": 49}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Yang et al . [ 41 ] proposed globally optimal ICP ( GO-ICP ) , which rationalizes the planning of ICP update tasks at different stages , and its biggest advantage is that it minimizes the local optimum .", "output": {"entities": {"method": [{"text": "globally optimal ICP", "start": 29, "end": 49}, {"text": "GO-ICP", "start": 52, "end": 58}, {"text": "ICP", "start": 98, "end": 101}]}, "relations": {"synonym of": [{"head": {"text": "GO-ICP", "start": 52, "end": 58}, "tail": {"text": "globally optimal ICP", "start": 29, "end": 49}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Bustos and Chin [ 6 ] presented guaranteed outlier removal ( GORE ) , which calculates the tight lower bound and tight upper bound for each correspondence and reduces the size of correspondence set by rejecting true outliers .", "output": {"entities": {"method": [{"text": "guaranteed outlier removal", "start": 32, "end": 58}, {"text": "GORE", "start": 61, "end": 65}]}, "relations": {"synonym of": [{"head": {"text": "GORE", "start": 61, "end": 65}, "tail": {"text": "guaranteed outlier removal", "start": 32, "end": 58}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Motivated by GORE , Li [ 21 ] proposed a polynomial time outlier removal method , which seeks the tight lower and upper bound by calculating the costs of correspondence matrix ( CM ) and augmented correspondence matrix ( ACM ) .", "output": {"entities": {"method": [{"text": "GORE", "start": 13, "end": 17}, {"text": "correspondence matrix", "start": 154, "end": 175}, {"text": "CM", "start": 178, "end": 180}, {"text": "augmented correspondence matrix", "start": 187, "end": 218}, {"text": "ACM", "start": 221, "end": 224}]}, "relations": {"synonym of": [{"head": {"text": "CM", "start": 178, "end": 180}, "tail": {"text": "correspondence matrix", "start": 154, "end": 175}}, {"head": {"text": "ACM", "start": 221, "end": 224}, "tail": {"text": "augmented correspondence matrix", "start": 187, "end": 218}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , BnB techniques are sensitive to the cardinality of the input and are time-consuming for large-scale inputs .", "output": {"entities": {"method": [{"text": "BnB", "start": 10, "end": 13}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In addition to geometric-only methods , recent works also adopt deep learning techniques to perform PCR .", "output": {"entities": {"method": [{"text": "geometric-only methods", "start": 15, "end": 37}, {"text": "deep learning", "start": 64, "end": 77}], "task": [{"text": "PCR", "start": 100, "end": 103}]}, "relations": {"used for": [{"head": {"text": "geometric-only methods", "start": 15, "end": 37}, "tail": {"text": "PCR", "start": 100, "end": 103}}, {"head": {"text": "deep learning", "start": 64, "end": 77}, "tail": {"text": "PCR", "start": 100, "end": 103}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "FCGF [ 10 ] computes the features in a single pass through a fully convolutional neural network without keypoint detection .", "output": {"entities": {"method": [{"text": "FCGF", "start": 0, "end": 4}, {"text": "fully convolutional neural network", "start": 61, "end": 95}], "task": [{"text": "keypoint detection", "start": 104, "end": 122}]}, "relations": {"part of": [{"head": {"text": "fully convolutional neural network", "start": 61, "end": 95}, "tail": {"text": "FCGF", "start": 0, "end": 4}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "D3Feat [ 4 ] uses a fully convolutional network to obtain local information of point clouds and a joint learning framework to achieve 3D local feature detection and description .", "output": {"entities": {"method": [{"text": "D3Feat", "start": 0, "end": 6}, {"text": "fully convolutional network", "start": 20, "end": 47}, {"text": "joint learning framework", "start": 98, "end": 122}], "task": [{"text": "3D local feature detection", "start": 134, "end": 160}]}, "relations": {"part of": [{"head": {"text": "fully convolutional network", "start": 20, "end": 47}, "tail": {"text": "D3Feat", "start": 0, "end": 6}}, {"head": {"text": "joint learning framework", "start": 98, "end": 122}, "tail": {"text": "D3Feat", "start": 0, "end": 6}}], "used for": [{"head": {"text": "joint learning framework", "start": 98, "end": 122}, "tail": {"text": "3D local feature detection", "start": 134, "end": 160}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Predator [ 18 ] applies an attention mechanism to extract salient points in overlapping regions of the point clouds , thus achieving robust registration in the presence of low overlap rates .", "output": {"entities": {"method": [{"text": "Predator", "start": 0, "end": 8}, {"text": "attention mechanism", "start": 27, "end": 46}], "task": [{"text": "registration", "start": 140, "end": 152}]}, "relations": {"part of": [{"head": {"text": "attention mechanism", "start": 27, "end": 46}, "tail": {"text": "Predator", "start": 0, "end": 8}}], "used for": [{"head": {"text": "Predator", "start": 0, "end": 8}, "tail": {"text": "registration", "start": 140, "end": 152}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Spinnet [ 1 ] extracts local features which are rotationally invariant and sufficiently informative to enable accurate registration .", "output": {"entities": {"method": [{"text": "Spinnet", "start": 0, "end": 7}], "task": [{"text": "registration", "start": 119, "end": 131}]}, "relations": {"used for": [{"head": {"text": "Spinnet", "start": 0, "end": 7}, "tail": {"text": "registration", "start": 119, "end": 131}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Deep global registration ( DGR ) [ 9 ] and 3DReg-Net [ 27 ] classify a given correspondence by training endto-end neural networks and using operators such as sparse convolution and point-by-point MLP .", "output": {"entities": {"method": [{"text": "Deep global registration", "start": 0, "end": 24}, {"text": "DGR", "start": 27, "end": 30}, {"text": "3DReg-Net", "start": 43, "end": 52}, {"text": "neural networks", "start": 114, "end": 129}, {"text": "sparse convolution", "start": 158, "end": 176}, {"text": "point-by-point MLP", "start": 181, "end": 199}]}, "relations": {"synonym of": [{"head": {"text": "DGR", "start": 27, "end": 30}, "tail": {"text": "Deep global registration", "start": 0, "end": 24}}], "part of": [{"head": {"text": "neural networks", "start": 114, "end": 129}, "tail": {"text": "Deep global registration", "start": 0, "end": 24}}, {"head": {"text": "neural networks", "start": 114, "end": 129}, "tail": {"text": "3DReg-Net", "start": 43, "end": 52}}, {"head": {"text": "sparse convolution", "start": 158, "end": 176}, "tail": {"text": "neural networks", "start": 114, "end": 129}}, {"head": {"text": "point-by-point MLP", "start": 181, "end": 199}, "tail": {"text": "neural networks", "start": 114, "end": 129}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "PointDSC [ 3 ] explicitly explores spatial consistency for removing outlier correspondences and 3D point cloud registration .", "output": {"entities": {"method": [{"text": "PointDSC", "start": 0, "end": 8}], "task": [{"text": "point cloud registration", "start": 99, "end": 123}]}, "relations": {"used for": [{"head": {"text": "PointDSC", "start": 0, "end": 8}, "tail": {"text": "point cloud registration", "start": 99, "end": 123}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Fu et al . [ 14 ] proposed a registration framework that utilizes deep graph matching ( RGM ) that can find robust and accurate pointto-point correspondences .", "output": {"entities": {"task": [{"text": "registration", "start": 29, "end": 41}], "method": [{"text": "deep graph matching", "start": 66, "end": 85}, {"text": "RGM", "start": 88, "end": 91}]}, "relations": {"used for": [{"head": {"text": "deep graph matching", "start": 66, "end": 85}, "tail": {"text": "registration", "start": 29, "end": 41}}], "synonym of": [{"head": {"text": "RGM", "start": 88, "end": 91}, "tail": {"text": "deep graph matching", "start": 66, "end": 85}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "More recently , several methods [ 29 , 43 ] follow the detection-free methods and estimate the transformation in an end-to-end way .", "output": {"entities": {"method": [{"text": "detection-free methods", "start": 55, "end": 77}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "CoFiNet [ 43 ] extracts correspondences from coarse to fine without keypoint detection .", "output": {"entities": {"method": [{"text": "CoFiNet", "start": 0, "end": 7}], "task": [{"text": "keypoint detection", "start": 68, "end": 86}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "GeoTransformer [ 29 ] learns geometric features for robust superpoint matching and is robust in low-overlap cases and invariant to rigid transformation .", "output": {"entities": {"method": [{"text": "GeoTransformer", "start": 0, "end": 14}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC ( c1 ) = ( c1 , c3 , c4 , c5 ) MAC ( c3 ) = ( c1 , c3 , c4 , c5 ) MAC ( c2 ) = ( c2 , c6 , c7 ) … MAC ( c9 ) = ( c1 , c9 , c10 ) MAC ( c10 ) = ( c1 , c9 , c10 ) Node-guided maximal cliques c7 c7 c6 c6 c2 c2 c5 c5 c4 c4 c3 c3 c1 c1 R3 , t3 c10 c10 c9 c9 c1 c1 While deep learning techniques have demonstrated a great potential for PCR , these methods require a large amount of training data and their generalization is not always promising .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}, {"text": "MAC", "start": 35, "end": 38}, {"text": "MAC", "start": 70, "end": 73}, {"text": "MAC", "start": 102, "end": 105}, {"text": "MAC", "start": 133, "end": 136}, {"text": "Node-guided maximal cliques", "start": 165, "end": 192}, {"text": "deep learning techniques", "start": 269, "end": 293}], "task": [{"text": "PCR", "start": 334, "end": 337}]}, "relations": {"used for": [{"head": {"text": "deep learning techniques", "start": 269, "end": 293}, "tail": {"text": "PCR", "start": 334, "end": 337}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By contrast , MAC does not require any training data and achieves more advanced performance than several deep-learned methods .", "output": {"entities": {"method": [{"text": "MAC", "start": 14, "end": 17}, {"text": "deep-learned methods", "start": 105, "end": 125}]}, "relations": {"compare with": [{"head": {"text": "MAC", "start": 14, "end": 17}, "tail": {"text": "deep-learned methods", "start": 105, "end": 125}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Moreover , MAC can be served as a drop-on module in deep learning frameworks to boost their performance .", "output": {"entities": {"method": [{"text": "MAC", "start": 11, "end": 14}, {"text": "deep learning", "start": 52, "end": 65}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 11, "end": 14}, "tail": {"text": "deep learning", "start": 52, "end": 65}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "An initial correspondence set C initial = { c } is formed by matching feature descriptors , where c = ( p s , p t ) .", "output": {"entities": {"method": [{"text": "matching feature descriptors", "start": 61, "end": 89}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC estimates the 6 - DoF pose transformation between P s and P t from C initial .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , we consider two approaches to construct a compatibility graph . • First Order Graph .", "output": {"entities": {"method": [{"text": "First Order Graph", "start": 73, "end": 90}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The first order graph ( FOG ) is constructed based on the rigid distance constraint between the correspondence pair ( c i , c j ) , which can be quantitatively measured as : EQUATION The compatibility score between c i and c j is given as : EQUATION where d cmp is a distance parameter .", "output": {"entities": {"method": [{"text": "first order graph", "start": 4, "end": 21}, {"text": "FOG", "start": 24, "end": 27}]}, "relations": {"synonym of": [{"head": {"text": "FOG", "start": 24, "end": 27}, "tail": {"text": "first order graph", "start": 4, "end": 21}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Since the compatibility graph is undirected , the weight matrix W F OG is symmetric . • Second Order Graph .", "output": {"entities": {"method": [{"text": "Second Order Graph", "start": 88, "end": 106}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The second order graph ( SOG ) evolves from FOG .", "output": {"entities": {"method": [{"text": "second order graph", "start": 4, "end": 22}, {"text": "SOG", "start": 25, "end": 28}, {"text": "FOG", "start": 44, "end": 47}]}, "relations": {"synonym of": [{"head": {"text": "SOG", "start": 25, "end": 28}, "tail": {"text": "second order graph", "start": 4, "end": 22}}], "subclass of": [{"head": {"text": "second order graph", "start": 4, "end": 22}, "tail": {"text": "FOG", "start": 44, "end": 47}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The weight matrix W SOG can be calculated as : EQUATION where ⊙ represents the element-wise product between two matrices .", "output": {"entities": {"method": [{"text": "SOG", "start": 20, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In Sec . 4.5 , we experimentally compare FOG and SOG in our MAC framework .", "output": {"entities": {"method": [{"text": "FOG", "start": 41, "end": 44}, {"text": "SOG", "start": 49, "end": 52}, {"text": "MAC", "start": 60, "end": 63}]}, "relations": {"compare with": [{"head": {"text": "FOG", "start": 41, "end": 44}, "tail": {"text": "SOG", "start": 49, "end": 52}}], "part of": [{"head": {"text": "SOG", "start": 49, "end": 52}, "tail": {"text": "MAC", "start": 60, "end": 63}}, {"head": {"text": "FOG", "start": 41, "end": 44}, "tail": {"text": "MAC", "start": 60, "end": 63}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A maximal clique is a clique that cannot be extended by adding any nodes .", "output": {"entities": {"method": [{"text": "maximal clique", "start": 2, "end": 16}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In particular , the maximal clique with the most nodes is the maximum clique of a graph .", "output": {"entities": {"method": [{"text": "maximal clique", "start": 20, "end": 34}, {"text": "maximum clique", "start": 62, "end": 76}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Searching for Maximal cliques .", "output": {"entities": {"method": [{"text": "Maximal cliques", "start": 14, "end": 29}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To generate hypotheses , RANSAC-based methods repeatedly take random samples from the correspondence set .", "output": {"entities": {"method": [{"text": "RANSAC-based methods", "start": 25, "end": 45}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Previous works [ 23 , 24 , 28 , 36 ] focus on searching for maximum cliques in the graph , however , the maximum clique is a very tight constraint that only focuses on the global consensus information in a graph .", "output": {"entities": {"method": [{"text": "maximum cliques", "start": 60, "end": 75}, {"text": "maximum clique", "start": 105, "end": 119}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Instead , we loosen the constraint and leverage maximal cliques to mine more local graph information .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 48, "end": 63}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By using the igraph maximal cliques function in the igraph 1 C + + library , which makes use of a modified Bron-Kerbosch algorithm [ 12 ] , the search of maximal cliques can be very efficient .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 20, "end": 35}, {"text": "Bron-Kerbosch algorithm", "start": 107, "end": 130}, {"text": "maximal cliques", "start": 154, "end": 169}]}, "relations": {"used for": [{"head": {"text": "Bron-Kerbosch algorithm", "start": 107, "end": 130}, "tail": {"text": "maximal cliques", "start": 20, "end": 35}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Node-guided Clique Selection .", "output": {"entities": {"method": [{"text": "Node-guided Clique Selection", "start": 0, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "First , we calculate the weight for each clique in MAC initial .", "output": {"entities": {"method": [{"text": "MAC", "start": 51, "end": 54}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Given a clique C i = ( V i , E i ) , the weight w Ci is calculated as : EQUATION where w ej represents the weight of edge e j in W SOG .", "output": {"entities": {"method": [{"text": "SOG", "start": 131, "end": 134}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A node may be included by multiple maximal cliques and we 1 https : / / igraph.org only retain the one with the greatest weight for that node .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 35, "end": 50}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Then , duplicated cliques are removed from the rest , obtaining MAC selected .", "output": {"entities": {"method": [{"text": "MAC", "start": 64, "end": 67}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We could send these maximal cliques directly to the following stages for 3D registration .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 20, "end": 35}], "task": [{"text": "3D registration", "start": 73, "end": 88}]}, "relations": {"used for": [{"head": {"text": "maximal cliques", "start": 20, "end": 35}, "tail": {"text": "3D registration", "start": 73, "end": 88}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , when | V | is quite large , the number of retained maximal cliques can still be very large .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 61, "end": 76}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , we propose several techniques to further filter the maximal cliques . • Normal consistency .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 59, "end": 74}, {"text": "Normal consistency", "start": 79, "end": 97}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In the maximal cliques , we find that the normal consistency is satisfied between each correspondence .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 7, "end": 22}, {"text": "normal consistency", "start": 42, "end": 60}]}, "relations": {"part of": [{"head": {"text": "normal consistency", "start": 42, "end": 60}, "tail": {"text": "maximal cliques", "start": 7, "end": 22}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The following inequality ought to hold if c i and c j are normal consistent : EQUATION where t α is a threshold for determining whether the angular differences are similar . • Clique ranking .", "output": {"entities": {"method": [{"text": "Clique ranking", "start": 176, "end": 190}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We organize MAC selected in a descending order using the clique's weight w Ci .", "output": {"entities": {"method": [{"text": "MAC", "start": 12, "end": 15}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Each maximal clique filtered from the previous step represents a consistent set of correspondences .", "output": {"entities": {"method": [{"text": "maximal clique", "start": 5, "end": 19}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By applying the SVD algorithm to each consistency set , we can obtain a set of 6 - DoF pose hypotheses . • Instance-equal SVD .", "output": {"entities": {"method": [{"text": "SVD", "start": 16, "end": 19}, {"text": "Instance-equal SVD", "start": 107, "end": 125}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Transformation estimation of correspondences is often implemented with SVD .", "output": {"entities": {"method": [{"text": "SVD", "start": 71, "end": 74}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Instance-equal means that the weights of all correspondences are equal . • Weighted SVD .", "output": {"entities": {"method": [{"text": "Weighted SVD", "start": 75, "end": 87}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Assigning weights to correspondences is commonly adopted by recent PCR methods [ 8 , 9 , 27 , 29 ] .", "output": {"entities": {"task": [{"text": "PCR", "start": 67, "end": 70}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , we take the primary eigenvalues of W SOG as correspondence weights .", "output": {"entities": {"method": [{"text": "SOG", "start": 44, "end": 47}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The final goal of MAC is to estimate the optimal 6 - DoF rigid transformation ( composed of a rotation pose R * ∈ SO ( 3 ) and a translation pose t * ∈ R 3 ) that maximizes the objective function as follow : EQUATION where c i ∈ C initial , N = | C initial | , and s ( c i ) represents the score of c i .", "output": {"entities": {"method": [{"text": "MAC", "start": 18, "end": 21}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We consider several RANSAC hypothesis evaluation metrics here [ 40 ] , including mean average error ( MAE ) , mean square error ( MSE ) and inlier count .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 20, "end": 26}, {"text": "mean average error", "start": 81, "end": 99}, {"text": "MAE", "start": 102, "end": 105}, {"text": "mean square error", "start": 110, "end": 127}, {"text": "MSE", "start": 130, "end": 133}, {"text": "inlier count", "start": 140, "end": 152}]}, "relations": {"synonym of": [{"head": {"text": "MAE", "start": 102, "end": 105}, "tail": {"text": "mean average error", "start": 81, "end": 99}}, {"head": {"text": "MSE", "start": 130, "end": 133}, "tail": {"text": "mean square error", "start": 110, "end": 127}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The best hypothesis is taken to perform 3D registration then .", "output": {"entities": {"task": [{"text": "3D registration", "start": 40, "end": 55}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We consider four datasets , i.e , the objectscale dataset U3M [ 26 ] , the scene-scale indoor datasets 3DMatch [ 44 ] & 3DLoMatch [ 18 ] , and the scene-scale outdoor dataset KITTI [ 15 ] .", "output": {"entities": {"dataset": [{"text": "U3M", "start": 58, "end": 61}, {"text": "3DMatch", "start": 103, "end": 110}, {"text": "3DLoMatch", "start": 120, "end": 129}, {"text": "KITTI", "start": 175, "end": 180}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "U3M has 496 point cloud pairs . 3DLoMatch is the subset of 3DMatch , where the overlap rate of the point cloud pairs ranges from 10% to 30% , which is very challenging .", "output": {"entities": {"dataset": [{"text": "U3M", "start": 0, "end": 3}, {"text": "3DLoMatch", "start": 32, "end": 41}, {"text": "3DMatch", "start": 59, "end": 66}]}, "relations": {"subclass of": [{"head": {"text": "3DLoMatch", "start": 32, "end": 41}, "tail": {"text": "3DMatch", "start": 59, "end": 66}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For KITTI , we follow [ 3 , 8 ] and obtain 555 pairs of point clouds for testing .", "output": {"entities": {"dataset": [{"text": "KITTI", "start": 4, "end": 9}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In addition , we employ the rotation error ( RE ) and translation error ( TE ) to evaluate the registration results on the scene-scale dataset .", "output": {"entities": {"task": [{"text": "registration", "start": 95, "end": 107}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "By referring to the settings in [ 9 ] , the registration is considered successful when the RE ≤ 15 ° , TE ≤ 30 cm on 3DMatch & 3DLoMatch datasets , and RE ≤ 5 ° , TE ≤ 60 cm on KITTI dataset .", "output": {"entities": {"task": [{"text": "registration", "start": 44, "end": 56}], "dataset": [{"text": "3DMatch", "start": 117, "end": 124}, {"text": "3DLoMatch", "start": 127, "end": 136}, {"text": "KITTI", "start": 177, "end": 182}]}, "relations": {"benchmark for": [{"head": {"text": "3DMatch", "start": 117, "end": 124}, "tail": {"text": "registration", "start": 44, "end": 56}}, {"head": {"text": "3DLoMatch", "start": 127, "end": 136}, "tail": {"text": "registration", "start": 44, "end": 56}}, {"head": {"text": "KITTI", "start": 177, "end": 182}, "tail": {"text": "registration", "start": 44, "end": 56}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We define a dataset's registration accuracy as the ratio of success cases to the number of point cloud pairs to be registered .", "output": {"entities": {"task": [{"text": "registration", "start": 22, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our method is implemented in C + + based on the point cloud library ( PCL ) [ 32 ] and igraph library .", "output": {"entities": {"method": [{"text": "point cloud library", "start": 48, "end": 67}, {"text": "PCL", "start": 70, "end": 73}]}, "relations": {"synonym of": [{"head": {"text": "PCL", "start": 70, "end": 73}, "tail": {"text": "point cloud library", "start": 48, "end": 67}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For U3M , we use the Harris3D ( H3D ) [ 33 ] keypoint detector and the signatures of histograms of orientation ( SHOT ) [ 34 ] descriptor for initial correspondence generation as in [ 42 ] .", "output": {"entities": {"dataset": [{"text": "U3M", "start": 4, "end": 7}], "method": [{"text": "Harris3D", "start": 21, "end": 29}, {"text": "H3D", "start": 32, "end": 35}, {"text": "signatures of histograms of orientation", "start": 71, "end": 110}, {"text": "SHOT", "start": 113, "end": 117}]}, "relations": {"evaluated with": [{"head": {"text": "Harris3D", "start": 21, "end": 29}, "tail": {"text": "U3M", "start": 4, "end": 7}}, {"head": {"text": "signatures of histograms of orientation", "start": 71, "end": 110}, "tail": {"text": "U3M", "start": 4, "end": 7}}], "synonym of": [{"head": {"text": "H3D", "start": 32, "end": 35}, "tail": {"text": "Harris3D", "start": 21, "end": 29}}, {"head": {"text": "SHOT", "start": 113, "end": 117}, "tail": {"text": "signatures of histograms of orientation", "start": 71, "end": 110}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For 3DMatch and 3DLoMatch datasets , we use the fast point features histograms ( FPFH ) [ 31 ] descriptor and fully convolutional geometric features ( FCGF ) [ 10 ] descriptor to generate the initial correspondence set .", "output": {"entities": {"dataset": [{"text": "3DMatch", "start": 4, "end": 11}, {"text": "3DLoMatch", "start": 16, "end": 25}], "method": [{"text": "fast point features histograms", "start": 48, "end": 78}, {"text": "FPFH", "start": 81, "end": 85}, {"text": "fully convolutional geometric features", "start": 110, "end": 148}, {"text": "FCGF", "start": 151, "end": 155}]}, "relations": {"evaluated with": [{"head": {"text": "fully convolutional geometric features", "start": 110, "end": 148}, "tail": {"text": "3DMatch", "start": 4, "end": 11}}, {"head": {"text": "fast point features histograms", "start": 48, "end": 78}, "tail": {"text": "3DMatch", "start": 4, "end": 11}}, {"head": {"text": "fast point features histograms", "start": 48, "end": 78}, "tail": {"text": "3DLoMatch", "start": 16, "end": 25}}, {"head": {"text": "fully convolutional geometric features", "start": 110, "end": 148}, "tail": {"text": "3DLoMatch", "start": 16, "end": 25}}], "synonym of": [{"head": {"text": "FPFH", "start": 81, "end": 85}, "tail": {"text": "fast point features histograms", "start": 48, "end": 78}}, {"head": {"text": "FCGF", "start": 151, "end": 155}, "tail": {"text": "fully convolutional geometric features", "start": 110, "end": 148}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here , the following methods are tested , including SAC-COT [ 39 ] , OSAC [ 37 ] , SAC-IA [ 31 ] , RANSAC [ 13 ] , SC 2 - PCR [ 8 ] , FGR [ 45 ] , GO-ICP [ 41 ] , and PPF [ 11 ] , where the former four are RANSAC-based methods .", "output": {"entities": {"method": [{"text": "SAC-COT", "start": 52, "end": 59}, {"text": "OSAC", "start": 69, "end": 73}, {"text": "SAC-IA", "start": 83, "end": 89}, {"text": "RANSAC", "start": 99, "end": 105}, {"text": "SC 2 - PCR", "start": 115, "end": 125}, {"text": "FGR", "start": 134, "end": 137}, {"text": "GO-ICP", "start": 147, "end": 153}, {"text": "PPF", "start": 167, "end": 170}, {"text": "RANSAC-based methods", "start": 206, "end": 226}]}, "relations": {"subclass of": [{"head": {"text": "SAC-COT", "start": 52, "end": 59}, "tail": {"text": "RANSAC-based methods", "start": 206, "end": 226}}, {"head": {"text": "OSAC", "start": 69, "end": 73}, "tail": {"text": "RANSAC-based methods", "start": 206, "end": 226}}, {"head": {"text": "SAC-IA", "start": 83, "end": 89}, "tail": {"text": "RANSAC-based methods", "start": 206, "end": 226}}, {"head": {"text": "RANSAC", "start": 99, "end": 105}, "tail": {"text": "RANSAC-based methods", "start": 206, "end": 226}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The results indicate that MAC performs best and significantly outperforms all tested RANSAC-fashion estimators , such as SAC-COT , OSAC , SAC-IA , and RANSAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 26, "end": 29}, {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}, {"text": "SAC-COT", "start": 121, "end": 128}, {"text": "OSAC", "start": 131, "end": 135}, {"text": "SAC-IA", "start": 138, "end": 144}, {"text": "RANSAC", "start": 151, "end": 157}]}, "relations": {"compare with": [{"head": {"text": "MAC", "start": 26, "end": 29}, "tail": {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}}, {"head": {"text": "MAC", "start": 26, "end": 29}, "tail": {"text": "SAC-COT", "start": 121, "end": 128}}, {"head": {"text": "MAC", "start": 26, "end": 29}, "tail": {"text": "OSAC", "start": 131, "end": 135}}, {"head": {"text": "MAC", "start": 26, "end": 29}, "tail": {"text": "SAC-IA", "start": 138, "end": 144}}, {"head": {"text": "MAC", "start": 26, "end": 29}, "tail": {"text": "RANSAC", "start": 151, "end": 157}}], "subclass of": [{"head": {"text": "SAC-COT", "start": 121, "end": 128}, "tail": {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}}, {"head": {"text": "OSAC", "start": 131, "end": 135}, "tail": {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}}, {"head": {"text": "SAC-IA", "start": 138, "end": 144}, "tail": {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}}, {"head": {"text": "RANSAC", "start": 151, "end": 157}, "tail": {"text": "RANSAC-fashion estimators", "start": 85, "end": 110}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The registration performance of MAC based on the MAE evaluation metric is the best on U3M .", "output": {"entities": {"task": [{"text": "registration", "start": 4, "end": 16}], "method": [{"text": "MAC", "start": 32, "end": 35}, {"text": "MAE", "start": 49, "end": 52}], "dataset": [{"text": "U3M", "start": 86, "end": 89}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 32, "end": 35}, "tail": {"text": "registration", "start": 4, "end": 16}}], "benchmark for": [{"head": {"text": "U3M", "start": 86, "end": 89}, "tail": {"text": "registration", "start": 4, "end": 16}}], "part of": [{"head": {"text": "MAE", "start": 49, "end": 52}, "tail": {"text": "MAC", "start": 32, "end": 35}}], "evaluated with": [{"head": {"text": "MAC", "start": 32, "end": 35}, "tail": {"text": "U3M", "start": 86, "end": 89}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "PCR methods comparison .", "output": {"entities": {"task": [{"text": "PCR", "start": 0, "end": 3}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Both geometric-only and deep-learned methods are considered for comparison , including SM [ 20 ] , FGR [ 45 ] , RANSAC [ 13 ] , TEASER + + [ 36 ] , CG-SAC [ 30 ] , SC 2 - PCR [ 8 ] , 3DRegNet [ 27 ] , DGR [ 9 ] , DHVR [ 19 ] and PointDSC [ 3 ] .", "output": {"entities": {"method": [{"text": "geometric-only", "start": 5, "end": 19}, {"text": "deep-learned methods", "start": 24, "end": 44}, {"text": "SM", "start": 87, "end": 89}, {"text": "FGR", "start": 99, "end": 102}, {"text": "RANSAC", "start": 112, "end": 118}, {"text": "TEASER + +", "start": 128, "end": 138}, {"text": "CG-SAC", "start": 148, "end": 154}, {"text": "SC 2 - PCR", "start": 164, "end": 174}, {"text": "3DRegNet", "start": 183, "end": 191}, {"text": "DGR", "start": 201, "end": 204}, {"text": "DHVR", "start": 213, "end": 217}, {"text": "PointDSC", "start": 229, "end": 237}]}, "relations": {"compare with": [{"head": {"text": "geometric-only", "start": 5, "end": 19}, "tail": {"text": "deep-learned methods", "start": 24, "end": 44}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The following conclusions can be made : 1 ) regardless of which descriptor is used , MAC outperforms all compared methods on both 3DMatch and 3DLoMatch datasets , indicating its strong ability to register indoor scene point clouds ; 2 ) even compared with deep-learned methods , MAC still achieves better performance without any data training ; 3 ) in addition to the registration recall ( RR ) metric , MAC achieves the best RE and TE metrics .", "output": {"entities": {"method": [{"text": "MAC", "start": 85, "end": 88}, {"text": "deep-learned methods", "start": 256, "end": 276}, {"text": "MAC", "start": 279, "end": 282}, {"text": "MAC", "start": 404, "end": 407}], "dataset": [{"text": "3DMatch", "start": 130, "end": 137}, {"text": "3DLoMatch", "start": 142, "end": 151}], "task": [{"text": "registration", "start": 368, "end": 380}]}, "relations": {"evaluated with": [{"head": {"text": "MAC", "start": 85, "end": 88}, "tail": {"text": "3DMatch", "start": 130, "end": 137}}, {"head": {"text": "MAC", "start": 85, "end": 88}, "tail": {"text": "3DLoMatch", "start": 142, "end": 151}}], "compare with": [{"head": {"text": "MAC", "start": 279, "end": 282}, "tail": {"text": "deep-learned methods", "start": 256, "end": 276}}], "used for": [{"head": {"text": "MAC", "start": 404, "end": 407}, "tail": {"text": "registration", "start": 368, "end": 380}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This indicates that registrations by MAC are very accurate and MAC is able to align low overlapping data .", "output": {"entities": {"task": [{"text": "registrations", "start": 20, "end": 33}], "method": [{"text": "MAC", "start": 37, "end": 40}, {"text": "MAC", "start": 63, "end": 66}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 37, "end": 40}, "tail": {"text": "registrations", "start": 20, "end": 33}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Boosting deep-learned methods with MAC .", "output": {"entities": {"method": [{"text": "deep-learned methods", "start": 9, "end": 29}, {"text": "MAC", "start": 35, "end": 38}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 35, "end": 38}, "tail": {"text": "deep-learned methods", "start": 9, "end": 29}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Several kinds of state-of-the-art deep-learned methods are integrated with MAC for evaluation .", "output": {"entities": {"method": [{"text": "deep-learned methods", "start": 34, "end": 54}, {"text": "MAC", "start": 75, "end": 78}]}, "relations": {"part of": [{"head": {"text": "MAC", "start": 75, "end": 78}, "tail": {"text": "deep-learned methods", "start": 34, "end": 54}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The considered methods are FCGF [ 10 ] , SpinNet [ 1 ] , Predator [ 18 ] , CoFiNet [ 43 ] and Geo-Transformer [ 29 ] .", "output": {"entities": {"method": [{"text": "FCGF", "start": 27, "end": 31}, {"text": "SpinNet", "start": 41, "end": 48}, {"text": "Predator", "start": 57, "end": 65}, {"text": "CoFiNet", "start": 75, "end": 82}, {"text": "Geo-Transformer", "start": 94, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Remarkably , MAC dramatically improves the registration recall under all tested methods on both 3DMatch and 3DLoMatch datasets .", "output": {"entities": {"method": [{"text": "MAC", "start": 13, "end": 16}], "task": [{"text": "registration", "start": 43, "end": 55}], "dataset": [{"text": "3DMatch", "start": 96, "end": 103}, {"text": "3DLoMatch", "start": 108, "end": 117}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 13, "end": 16}, "tail": {"text": "registration", "start": 43, "end": 55}}], "benchmark for": [{"head": {"text": "3DLoMatch", "start": 108, "end": 117}, "tail": {"text": "registration", "start": 43, "end": 55}}, {"head": {"text": "3DMatch", "start": 96, "end": 103}, "tail": {"text": "registration", "start": 43, "end": 55}}], "evaluated with": [{"head": {"text": "MAC", "start": 13, "end": 16}, "tail": {"text": "3DMatch", "start": 96, "end": 103}}, {"head": {"text": "MAC", "start": 13, "end": 16}, "tail": {"text": "3DLoMatch", "start": 108, "end": 117}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Notably , the performance of Spin-Net , Predator and CoFiNet after boosting by MAC exceeds In Table 4 , the results of DGR [ 9 ] , PointDSC [ 3 ] , TEASER + + [ 36 ] , RANSAC [ 13 ] , CG-SAC [ 30 ] , SC 2 - PCR [ 8 ] and MAC are reported for comparison .", "output": {"entities": {"method": [{"text": "Spin-Net", "start": 29, "end": 37}, {"text": "Predator", "start": 40, "end": 48}, {"text": "CoFiNet", "start": 53, "end": 60}, {"text": "MAC", "start": 79, "end": 82}, {"text": "DGR", "start": 119, "end": 122}, {"text": "PointDSC", "start": 131, "end": 139}, {"text": "TEASER + +", "start": 148, "end": 158}, {"text": "RANSAC", "start": 168, "end": 174}, {"text": "CG-SAC", "start": 184, "end": 190}, {"text": "SC 2 - PCR", "start": 200, "end": 210}, {"text": "MAC", "start": 221, "end": 224}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As shown by the table , in terms of the registration recall performance , MAC presents the best and is tied for the best results with FPFH and FCGF descriptor settings , respectively .", "output": {"entities": {"task": [{"text": "registration", "start": 40, "end": 52}], "method": [{"text": "MAC", "start": 74, "end": 77}, {"text": "FPFH", "start": 134, "end": 138}, {"text": "FCGF", "start": 143, "end": 147}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 74, "end": 77}, "tail": {"text": "registration", "start": 40, "end": 52}}], "part of": [{"head": {"text": "FPFH", "start": 134, "end": 138}, "tail": {"text": "MAC", "start": 74, "end": 77}}, {"head": {"text": "FCGF", "start": 143, "end": 147}, "tail": {"text": "MAC", "start": 74, "end": 77}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAC also has a lower TE than the state-ofthe-art geometric-only method SC 2 - PCR .", "output": {"entities": {"method": [{"text": "MAC", "start": 0, "end": 3}, {"text": "geometric-only method", "start": 49, "end": 70}, {"text": "SC 2 - PCR", "start": 71, "end": 81}]}, "relations": {"compare with": [{"head": {"text": "MAC", "start": 0, "end": 3}, "tail": {"text": "geometric-only method", "start": 49, "end": 70}}, {"head": {"text": "MAC", "start": 0, "end": 3}, "tail": {"text": "SC 2 - PCR", "start": 71, "end": 81}}], "subclass of": [{"head": {"text": "SC 2 - PCR", "start": 71, "end": 81}, "tail": {"text": "geometric-only method", "start": 49, "end": 70}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The registration experiments on the object , indoor scene , and outdoor scene datasets consistently verify that MAC holds good generalization ability in different application contexts .", "output": {"entities": {"task": [{"text": "registration", "start": 4, "end": 16}], "method": [{"text": "MAC", "start": 112, "end": 115}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 112, "end": 115}, "tail": {"text": "registration", "start": 4, "end": 16}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this section , we perform ablation studies and analysis experiments on both 3DMatch and 3DLoMatch datasets .", "output": {"entities": {"dataset": [{"text": "3DMatch", "start": 79, "end": 86}, {"text": "3DLoMatch", "start": 91, "end": 100}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Table 8 presents the time efficiency analysis of MAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 49, "end": 52}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Performing feature matching selection .", "output": {"entities": {"task": [{"text": "feature matching selection", "start": 11, "end": 37}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Before 3D registration , a popular way is to perform outlier rejection to reduce the correspondence set .", "output": {"entities": {"task": [{"text": "3D registration", "start": 7, "end": 22}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here we employ geometric consistency ( GC ) [ 7 ] , which is independent of the feature space and associates the largest consistent cluster relating to the compatibility among correspondences .", "output": {"entities": {"method": [{"text": "geometric consistency", "start": 15, "end": 36}, {"text": "GC", "start": 39, "end": 41}]}, "relations": {"synonym of": [{"head": {"text": "GC", "start": 39, "end": 41}, "tail": {"text": "geometric consistency", "start": 15, "end": 36}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This demonstrates that MAC can still perform well even if the initial correspondence set is directly utilized as input without any filtering .", "output": {"entities": {"method": [{"text": "MAC", "start": 23, "end": 26}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We test the performance of MAC by using different graph construction approaches .", "output": {"entities": {"method": [{"text": "MAC", "start": 27, "end": 30}, {"text": "graph construction approaches", "start": 50, "end": 79}]}, "relations": {"part of": [{"head": {"text": "graph construction approaches", "start": 50, "end": 79}, "tail": {"text": "MAC", "start": 27, "end": 30}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Also , the registration recall obtained by using SOG is 0.12% higher than using FOG when combined with FPFH , and 0.56% higher when combined with FCGF on 3DLoMatch .", "output": {"entities": {"task": [{"text": "registration", "start": 11, "end": 23}], "method": [{"text": "SOG", "start": 49, "end": 52}, {"text": "FOG", "start": 80, "end": 83}, {"text": "FPFH", "start": 103, "end": 107}, {"text": "FCGF", "start": 146, "end": 150}], "dataset": [{"text": "3DLoMatch", "start": 154, "end": 163}]}, "relations": {"used for": [{"head": {"text": "SOG", "start": 49, "end": 52}, "tail": {"text": "registration", "start": 11, "end": 23}}, {"head": {"text": "FOG", "start": 80, "end": 83}, "tail": {"text": "registration", "start": 11, "end": 23}}], "benchmark for": [{"head": {"text": "3DLoMatch", "start": 154, "end": 163}, "tail": {"text": "registration", "start": 11, "end": 23}}], "part of": [{"head": {"text": "FPFH", "start": 103, "end": 107}, "tail": {"text": "SOG", "start": 49, "end": 52}}, {"head": {"text": "FCGF", "start": 146, "end": 150}, "tail": {"text": "SOG", "start": 49, "end": 52}}, {"head": {"text": "FPFH", "start": 103, "end": 107}, "tail": {"text": "FOG", "start": 80, "end": 83}}, {"head": {"text": "FCGF", "start": 146, "end": 150}, "tail": {"text": "FOG", "start": 80, "end": 83}}], "compare with": [{"head": {"text": "SOG", "start": 49, "end": 52}, "tail": {"text": "FOG", "start": 80, "end": 83}}], "evaluated with": [{"head": {"text": "SOG", "start": 49, "end": 52}, "tail": {"text": "3DLoMatch", "start": 154, "end": 163}}, {"head": {"text": "FOG", "start": 80, "end": 83}, "tail": {"text": "3DLoMatch", "start": 154, "end": 163}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Therefore , SOG is more suitable for MAC .", "output": {"entities": {"method": [{"text": "SOG", "start": 12, "end": 15}, {"text": "MAC", "start": 37, "end": 40}]}, "relations": {"part of": [{"head": {"text": "SOG", "start": 12, "end": 15}, "tail": {"text": "MAC", "start": 37, "end": 40}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Maximum or maximal clique .", "output": {"entities": {"method": [{"text": "maximal clique", "start": 11, "end": 25}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To justify the advantages of maximal cliques , we change the search strategy of MAC to the maximum cliques and test the registration performance .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 29, "end": 44}, {"text": "MAC", "start": 80, "end": 83}, {"text": "maximum cliques", "start": 91, "end": 106}], "task": [{"text": "registration", "start": 120, "end": 132}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 80, "end": 83}, "tail": {"text": "registration", "start": 120, "end": 132}}, {"head": {"text": "maximum cliques", "start": 91, "end": 106}, "tail": {"text": "registration", "start": 120, "end": 132}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As shown in Row 1 and 9 in Table 5 , applying maximal cliques surpasses maximum by 9.8% when combined with FPFH , and 5.55% higher when combined with FCGF on 3DMatch .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 46, "end": 61}, {"text": "FPFH", "start": 107, "end": 111}, {"text": "FCGF", "start": 150, "end": 154}], "dataset": [{"text": "3DMatch", "start": 158, "end": 165}]}, "relations": {"compare with": [{"head": {"text": "FPFH", "start": 107, "end": 111}, "tail": {"text": "FCGF", "start": 150, "end": 154}}], "evaluated with": [{"head": {"text": "FPFH", "start": 107, "end": 111}, "tail": {"text": "3DMatch", "start": 158, "end": 165}}, {"head": {"text": "FCGF", "start": 150, "end": 154}, "tail": {"text": "3DMatch", "start": 158, "end": 165}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Besides , the registration recall obtained by using maximal cliques is 8.03% higher than using the maximum cliques when combined with FPFH and 10.45% higher when combined with FCGF on 3DLoMatch .", "output": {"entities": {"task": [{"text": "registration", "start": 14, "end": 26}], "method": [{"text": "maximal cliques", "start": 52, "end": 67}, {"text": "maximum cliques", "start": 99, "end": 114}, {"text": "FPFH", "start": 134, "end": 138}, {"text": "FCGF", "start": 176, "end": 180}], "dataset": [{"text": "3DLoMatch", "start": 184, "end": 193}]}, "relations": {"used for": [{"head": {"text": "maximal cliques", "start": 52, "end": 67}, "tail": {"text": "registration", "start": 14, "end": 26}}], "part of": [{"head": {"text": "FCGF", "start": 176, "end": 180}, "tail": {"text": "maximal cliques", "start": 52, "end": 67}}, {"head": {"text": "FPFH", "start": 134, "end": 138}, "tail": {"text": "maximal cliques", "start": 52, "end": 67}}, {"head": {"text": "FPFH", "start": 134, "end": 138}, "tail": {"text": "maximum cliques", "start": 99, "end": 114}}, {"head": {"text": "FCGF", "start": 176, "end": 180}, "tail": {"text": "maximum cliques", "start": 99, "end": 114}}], "evaluated with": [{"head": {"text": "3DLoMatch", "start": 184, "end": 193}, "tail": {"text": "maximum cliques", "start": 99, "end": 114}}, {"head": {"text": "FPFH", "start": 134, "end": 138}, "tail": {"text": "3DLoMatch", "start": 184, "end": 193}}, {"head": {"text": "FCGF", "start": 176, "end": 180}, "tail": {"text": "3DLoMatch", "start": 184, "end": 193}}], "compare with": [{"head": {"text": "FPFH", "start": 134, "end": 138}, "tail": {"text": "FCGF", "start": 176, "end": 180}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "There are several reasons for this : 1 ) maximal cliques include the maximum cliques and additionally consider local graph constraints , so the search for maximal cliques can make use of both local and global information in the compatibility graph ; 2 ) the maximum clique is a very tight constraint which requires maximizing the number of mutually compatible correspondences , but it does not guarantee the opti-mal result .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 41, "end": 56}, {"text": "maximum cliques", "start": 69, "end": 84}, {"text": "maximal cliques", "start": 155, "end": 170}, {"text": "maximum clique", "start": 258, "end": 272}]}, "relations": {"part of": [{"head": {"text": "maximum cliques", "start": 69, "end": 84}, "tail": {"text": "maximal cliques", "start": 41, "end": 56}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Node-guided clique selection .", "output": {"entities": {"task": [{"text": "Node-guided clique selection", "start": 0, "end": 28}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We compare the performance with and without node-guided ( NG ) clique selection for maximal cliques search .", "output": {"entities": {"method": [{"text": "node-guided", "start": 44, "end": 55}, {"text": "NG", "start": 58, "end": 60}, {"text": "maximal cliques", "start": 84, "end": 99}]}, "relations": {"synonym of": [{"head": {"text": "NG", "start": 58, "end": 60}, "tail": {"text": "node-guided", "start": 44, "end": 55}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Comparing Row 1 and 4 in Table 5 , using NG achieves a recall improvement of 0.37% when combined with FPFH , and 0.5% improvement when combined with FCGF on 3DMatch .", "output": {"entities": {"method": [{"text": "NG", "start": 41, "end": 43}, {"text": "FPFH", "start": 102, "end": 106}, {"text": "FCGF", "start": 149, "end": 153}], "dataset": [{"text": "3DMatch", "start": 157, "end": 164}]}, "relations": {"part of": [{"head": {"text": "FPFH", "start": 102, "end": 106}, "tail": {"text": "NG", "start": 41, "end": 43}}, {"head": {"text": "FCGF", "start": 149, "end": 153}, "tail": {"text": "NG", "start": 41, "end": 43}}], "evaluated with": [{"head": {"text": "NG", "start": 41, "end": 43}, "tail": {"text": "3DMatch", "start": 157, "end": 164}}, {"head": {"text": "FPFH", "start": 102, "end": 106}, "tail": {"text": "3DMatch", "start": 157, "end": 164}}, {"head": {"text": "FCGF", "start": 149, "end": 153}, "tail": {"text": "3DMatch", "start": 157, "end": 164}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Also , using NG achieves a recall improvement of 0.23% with FPFH and 0.73% improvement with FCGF on 3DLoMatch .", "output": {"entities": {"method": [{"text": "NG", "start": 13, "end": 15}, {"text": "FPFH", "start": 60, "end": 64}, {"text": "FCGF", "start": 92, "end": 96}], "dataset": [{"text": "3DLoMatch", "start": 100, "end": 109}]}, "relations": {"part of": [{"head": {"text": "FPFH", "start": 60, "end": 64}, "tail": {"text": "NG", "start": 13, "end": 15}}, {"head": {"text": "FCGF", "start": 92, "end": 96}, "tail": {"text": "NG", "start": 13, "end": 15}}], "evaluated with": [{"head": {"text": "NG", "start": 13, "end": 15}, "tail": {"text": "3DLoMatch", "start": 100, "end": 109}}, {"head": {"text": "FPFH", "start": 60, "end": 64}, "tail": {"text": "3DLoMatch", "start": 100, "end": 109}}, {"head": {"text": "FCGF", "start": 92, "end": 96}, "tail": {"text": "3DLoMatch", "start": 100, "end": 109}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "It is worth noting that while NG improves recall , the mean RE and mean TE are also decreasing .", "output": {"entities": {"method": [{"text": "NG", "start": 30, "end": 32}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For example , NG reduces the mean RE by 0.1 ° and the mean TE by 0.11 cm with FPFH on 3DLoMatch .", "output": {"entities": {"method": [{"text": "NG", "start": 14, "end": 16}, {"text": "FPFH", "start": 78, "end": 82}], "dataset": [{"text": "3DLoMatch", "start": 86, "end": 95}]}, "relations": {"part of": [{"head": {"text": "FPFH", "start": 78, "end": 82}, "tail": {"text": "NG", "start": 14, "end": 16}}], "evaluated with": [{"head": {"text": "NG", "start": 14, "end": 16}, "tail": {"text": "3DLoMatch", "start": 86, "end": 95}}, {"head": {"text": "FPFH", "start": 78, "end": 82}, "tail": {"text": "3DLoMatch", "start": 86, "end": 95}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Different approaches for clique filtering .", "output": {"entities": {"method": [{"text": "clique filtering", "start": 25, "end": 41}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We test the effectiveness of the two filtering methods , normal consistency and clique ranking . 1 ) Normal consistency : comparing Row 1 and 8 in Table 5 , NC slightly degrades MAC's performance . 2 ) Clique ranking : Row 10 to 14 demonstrate that the registration recall tends to increase as K increases , suggesting that larger K yields a subset of cliques that generate more correct hypotheses .", "output": {"entities": {"method": [{"text": "normal consistency", "start": 57, "end": 75}, {"text": "clique ranking", "start": 80, "end": 94}, {"text": "Normal consistency", "start": 101, "end": 119}, {"text": "NC", "start": 157, "end": 159}, {"text": "MAC's", "start": 178, "end": 183}, {"text": "Clique ranking", "start": 202, "end": 216}], "task": [{"text": "registration", "start": 253, "end": 265}]}, "relations": {"part of": [{"head": {"text": "NC", "start": 157, "end": 159}, "tail": {"text": "MAC's", "start": 178, "end": 183}}], "used for": [{"head": {"text": "Clique ranking", "start": 202, "end": 216}, "tail": {"text": "registration", "start": 253, "end": 265}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Employing instance-equal or weighted SVD .", "output": {"entities": {"method": [{"text": "weighted SVD", "start": 28, "end": 40}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The comparisons of instance-equal and weighted SVD are shown in Rows 1 and 5 of Table 5 Weighted SVD is slightly inferior to instance-equal SVD .", "output": {"entities": {"method": [{"text": "weighted SVD", "start": 38, "end": 50}, {"text": "Weighted SVD", "start": 88, "end": 100}, {"text": "instance-equal SVD", "start": 125, "end": 143}]}, "relations": {"compare with": [{"head": {"text": "Weighted SVD", "start": 88, "end": 100}, "tail": {"text": "instance-equal SVD", "start": 125, "end": 143}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This suggests that samples in MACs are already very consistent , indicating no additional weighting strategies are required .", "output": {"entities": {"method": [{"text": "MACs", "start": 30, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Here we compare three evaluation metrics , including MAE , MSE and inlier count , for MAC hypothesis evaluation .", "output": {"entities": {"method": [{"text": "MAE", "start": 53, "end": 56}, {"text": "MSE", "start": 59, "end": 62}, {"text": "inlier count", "start": 67, "end": 79}, {"text": "MAC", "start": 86, "end": 89}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As shown in Row 1 , 6 and 7 , MAC with MAE achieves the best performance .", "output": {"entities": {"method": [{"text": "MAC", "start": 30, "end": 33}, {"text": "MAE", "start": 39, "end": 42}]}, "relations": {"part of": [{"head": {"text": "MAE", "start": 39, "end": 42}, "tail": {"text": "MAC", "start": 30, "end": 33}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In Table 5 , MAE achieves a recall improvement of 0.24% when combined with FPFH , and 0.31% improvement when combined with FCGF on 3DMatch compared with the commonly used inlier count metric .", "output": {"entities": {"method": [{"text": "MAE", "start": 13, "end": 16}, {"text": "FPFH", "start": 75, "end": 79}, {"text": "FCGF", "start": 123, "end": 127}, {"text": "inlier count", "start": 171, "end": 183}], "dataset": [{"text": "3DMatch", "start": 131, "end": 138}]}, "relations": {"part of": [{"head": {"text": "FPFH", "start": 75, "end": 79}, "tail": {"text": "MAE", "start": 13, "end": 16}}, {"head": {"text": "FCGF", "start": 123, "end": 127}, "tail": {"text": "MAE", "start": 13, "end": 16}}], "compare with": [{"head": {"text": "FPFH", "start": 75, "end": 79}, "tail": {"text": "FCGF", "start": 123, "end": 127}}, {"head": {"text": "MAE", "start": 13, "end": 16}, "tail": {"text": "inlier count", "start": 171, "end": 183}}], "evaluated with": [{"head": {"text": "FPFH", "start": 75, "end": 79}, "tail": {"text": "3DMatch", "start": 131, "end": 138}}, {"head": {"text": "FCGF", "start": 123, "end": 127}, "tail": {"text": "3DMatch", "start": 131, "end": 138}}, {"head": {"text": "MAE", "start": 13, "end": 16}, "tail": {"text": "3DMatch", "start": 131, "end": 138}}, {"head": {"text": "inlier count", "start": 171, "end": 183}, "tail": {"text": "3DMatch", "start": 131, "end": 138}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Also , MAE has a 1.74% improvement when combined with FPFH , and 0.05% when combined with FCGF on 3DLoMatch compared with inlier count .", "output": {"entities": {"method": [{"text": "MAE", "start": 7, "end": 10}, {"text": "FPFH", "start": 54, "end": 58}, {"text": "FCGF", "start": 90, "end": 94}, {"text": "inlier count", "start": 122, "end": 134}], "dataset": [{"text": "3DLoMatch", "start": 98, "end": 107}]}, "relations": {"part of": [{"head": {"text": "FPFH", "start": 54, "end": 58}, "tail": {"text": "MAE", "start": 7, "end": 10}}, {"head": {"text": "FCGF", "start": 90, "end": 94}, "tail": {"text": "MAE", "start": 7, "end": 10}}], "compare with": [{"head": {"text": "FPFH", "start": 54, "end": 58}, "tail": {"text": "FCGF", "start": 90, "end": 94}}, {"head": {"text": "MAE", "start": 7, "end": 10}, "tail": {"text": "inlier count", "start": 122, "end": 134}}], "evaluated with": [{"head": {"text": "MAE", "start": 7, "end": 10}, "tail": {"text": "3DLoMatch", "start": 98, "end": 107}}, {"head": {"text": "FCGF", "start": 90, "end": 94}, "tail": {"text": "3DLoMatch", "start": 98, "end": 107}}, {"head": {"text": "inlier count", "start": 122, "end": 134}, "tail": {"text": "3DLoMatch", "start": 98, "end": 107}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MAE is also very effective in reducing RE and TE .", "output": {"entities": {"method": [{"text": "MAE", "start": 0, "end": 3}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Comparison with RANSAC hypotheses .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 16, "end": 22}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We evaluate the quality of the generated hypotheses by comparing the hypotheses from RANSAC and MAC with the ground truth transformation .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 85, "end": 91}, {"text": "MAC", "start": 96, "end": 99}]}, "relations": {"compare with": [{"head": {"text": "RANSAC", "start": 85, "end": 91}, "tail": {"text": "MAC", "start": 96, "end": 99}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Compared to RANSAC , which randomly selects correspondences and generates hypotheses from the correspondence set without geometric constraints , MAC effectively generates more convincing hypotheses from maximal cliques in the compatibility graph , which fully exploits the consensus information in the graph .", "output": {"entities": {"method": [{"text": "RANSAC", "start": 12, "end": 18}, {"text": "MAC", "start": 145, "end": 148}, {"text": "maximal cliques", "start": 203, "end": 218}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The performance upper bound of MAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 31, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This can test the performance upper bound of MAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 45, "end": 48}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We vary the judging threshold for the number of generated correct hypotheses and report the results in Impressively , MAC - 1 achieves registration recalls of 98.46% / 91.24% on 3DMatch / 3DLoMatch .", "output": {"entities": {"method": [{"text": "MAC", "start": 118, "end": 121}], "task": [{"text": "registration", "start": 135, "end": 147}], "dataset": [{"text": "3DMatch", "start": 178, "end": 185}, {"text": "3DLoMatch", "start": 188, "end": 197}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 118, "end": 121}, "tail": {"text": "registration", "start": 135, "end": 147}}], "benchmark for": [{"head": {"text": "3DLoMatch", "start": 188, "end": 197}, "tail": {"text": "registration", "start": 135, "end": 147}}, {"head": {"text": "3DMatch", "start": 178, "end": 185}, "tail": {"text": "registration", "start": 135, "end": 147}}], "evaluated with": [{"head": {"text": "MAC", "start": 118, "end": 121}, "tail": {"text": "3DMatch", "start": 178, "end": 185}}, {"head": {"text": "MAC", "start": 118, "end": 121}, "tail": {"text": "3DLoMatch", "start": 188, "end": 197}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This indicates that even on low overlapping datasets , MAC is able to produce correct hypotheses for most point cloud pairs .", "output": {"entities": {"method": [{"text": "MAC", "start": 55, "end": 58}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In addition , we can deduce that MAC's performance can be further improved with better hypothesis evaluation metrics .", "output": {"entities": {"method": [{"text": "MAC's", "start": 33, "end": 38}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Time consumption of MAC .", "output": {"entities": {"method": [{"text": "MAC", "start": 20, "end": 23}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The following observations can be made . 1 ) In general , MAC can complete 3D registration in only tens of milliseconds when the number of correspondences is smaller than 1000 .", "output": {"entities": {"method": [{"text": "MAC", "start": 58, "end": 61}], "task": [{"text": "3D registration", "start": 75, "end": 90}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 58, "end": 61}, "tail": {"text": "3D registration", "start": 75, "end": 90}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Note that MAC is implemented on the CPU only . 2 ) As the number of correspondences increases from 250 to 2500 , there is an increase in time cost for graph construction due to W SOG computation taking more time . 3 ) When the number of correspondences reaches 5000 , there is a large rise in the time cost of MAC's registration .", "output": {"entities": {"method": [{"text": "MAC", "start": 10, "end": 13}, {"text": "SOG", "start": 179, "end": 182}, {"text": "MAC's", "start": 310, "end": 315}], "task": [{"text": "registration", "start": 316, "end": 328}]}, "relations": {"used for": [{"head": {"text": "MAC's", "start": 310, "end": 315}, "tail": {"text": "registration", "start": 316, "end": 328}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The significant increase in the input size makes the search for maximal cliques more timeconsuming .", "output": {"entities": {"method": [{"text": "maximal cliques", "start": 64, "end": 79}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "However , MAC is not sensitive to the cardinality of the input correspondence set , as verified in Table 3 .", "output": {"entities": {"method": [{"text": "MAC", "start": 10, "end": 13}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Hence , using sparse inputs for MAC can produce outstanding performance while making registration efficient .", "output": {"entities": {"method": [{"text": "MAC", "start": 32, "end": 35}], "task": [{"text": "registration", "start": 85, "end": 97}]}, "relations": {"used for": [{"head": {"text": "MAC", "start": 32, "end": 35}, "tail": {"text": "registration", "start": 85, "end": 97}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this paper , we presented MAC to solve PCR by using the maximal clique constraint to generate precise pose hypotheses from correspondences .", "output": {"entities": {"method": [{"text": "MAC", "start": 29, "end": 32}, {"text": "maximal clique", "start": 59, "end": 73}], "task": [{"text": "PCR", "start": 42, "end": 45}]}, "relations": {"part of": [{"head": {"text": "maximal clique", "start": 59, "end": 73}, "tail": {"text": "MAC", "start": 29, "end": 32}}], "used for": [{"head": {"text": "MAC", "start": 29, "end": 32}, "tail": {"text": "PCR", "start": 42, "end": 45}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our method achieves state-of-the-art performance on all tested datasets and can adapt to deep-learned methods to boost their performance .", "output": {"entities": {"method": [{"text": "deep-learned methods", "start": 89, "end": 109}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "As shown in Table 7 and Table 1 , MAC produces accurate hypotheses but may fail to find them .", "output": {"entities": {"method": [{"text": "MAC", "start": 34, "end": 37}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDIFF : COARSE-GRAINED DIFFUSION FOR METAL-ORGANIC FRAMEWORK DESIGN Metal-organic frameworks ( MOFs ) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry .", "output": {"entities": {"method": [{"text": "MOFDIFF", "start": 0, "end": 7}, {"text": "COARSE-GRAINED DIFFUSION", "start": 10, "end": 34}, {"text": "Metal-organic frameworks", "start": 70, "end": 94}, {"text": "MOFs", "start": 97, "end": 101}], "task": [{"text": "METAL-ORGANIC FRAMEWORK DESIGN", "start": 39, "end": 69}, {"text": "gas storage", "start": 152, "end": 163}, {"text": "carbon capture", "start": 168, "end": 182}]}, "relations": {"used for": [{"head": {"text": "COARSE-GRAINED DIFFUSION", "start": 10, "end": 34}, "tail": {"text": "METAL-ORGANIC FRAMEWORK DESIGN", "start": 39, "end": 69}}, {"head": {"text": "Metal-organic frameworks", "start": 70, "end": 94}, "tail": {"text": "gas storage", "start": 152, "end": 163}}, {"head": {"text": "Metal-organic frameworks", "start": 70, "end": 94}, "tail": {"text": "carbon capture", "start": 168, "end": 182}}], "synonym of": [{"head": {"text": "MOFs", "start": 97, "end": 101}, "tail": {"text": "Metal-organic frameworks", "start": 70, "end": 94}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In this work , we propose MOFDiff : a coarse-grained ( CG ) diffusion model that generates CG MOF structures through a denoising diffusion process over the coordinates and identities of the building blocks .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 26, "end": 33}, {"text": "coarse-grained ( CG ) diffusion model", "start": 38, "end": 75}], "task": [{"text": "CG MOF", "start": 91, "end": 97}]}, "relations": {"subclass of": [{"head": {"text": "MOFDiff", "start": 26, "end": 33}, "tail": {"text": "coarse-grained ( CG ) diffusion model", "start": 38, "end": 75}}], "used for": [{"head": {"text": "MOFDiff", "start": 26, "end": 33}, "tail": {"text": "CG MOF", "start": 91, "end": 97}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Equivariant graph neural networks are used for the diffusion model to respect the permutational and roto-translational symmetries .", "output": {"entities": {"method": [{"text": "Equivariant graph neural networks", "start": 0, "end": 33}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A denoising diffusion process conditional on z generates the building block identities and coordinates .", "output": {"entities": {"method": [{"text": "denoising diffusion process", "start": 2, "end": 29}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "To address the challenges above , we propose MOFDiff , a coarse-grained diffusion model for generating 3D MOF structures that leverages the modular and hierarchical structure of MOFs ( Figure 1 ( a ) ) .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 45, "end": 52}, {"text": "coarse-grained diffusion model", "start": 57, "end": 87}]}, "relations": {"subclass of": [{"head": {"text": "MOFDiff", "start": 45, "end": 52}, "tail": {"text": "coarse-grained diffusion model", "start": 57, "end": 87}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We train MOFDiff on BW-DB and use MOFDiff to generate and optimize MOF structures for carbon capture .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 9, "end": 16}, {"text": "MOFDiff", "start": 34, "end": 41}], "dataset": [{"text": "BW-DB", "start": 20, "end": 25}], "task": [{"text": "carbon capture", "start": 86, "end": 100}]}, "relations": {"trained with": [{"head": {"text": "MOFDiff", "start": 9, "end": 16}, "tail": {"text": "BW-DB", "start": 20, "end": 25}}], "used for": [{"head": {"text": "MOFDiff", "start": 34, "end": 41}, "tail": {"text": "carbon capture", "start": 86, "end": 100}}], "benchmark for": [{"head": {"text": "BW-DB", "start": 20, "end": 25}, "tail": {"text": "carbon capture", "start": 86, "end": 100}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We propose to learn a contrastive embedding to represent the vast building block design space . • We formulate a diffusion process for generating coarse-grained MOF 3D structures .", "output": {"entities": {"method": [{"text": "contrastive embedding", "start": 22, "end": 43}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In order to construct a compact representation of building blocks for diffusion-based modeling , we use a contrastive learning approach [ Hadsell et al . , 2006 , Chen et al . , 2020 ] to embed building blocks into a low dimensional latent space .", "output": {"entities": {"task": [{"text": "diffusion-based modeling", "start": 70, "end": 94}], "method": [{"text": "contrastive learning", "start": 106, "end": 126}]}, "relations": {"used for": [{"head": {"text": "contrastive learning", "start": 106, "end": 126}, "tail": {"text": "diffusion-based modeling", "start": 70, "end": 94}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "A building block i is encoded as a vector b i using a GemNet-OC encoder [ Gasteiger et al . , 2021 [ Gasteiger et al . , , 2022 ] ] , an SE ( 3 ) - invariant graph neural network model .", "output": {"entities": {"method": [{"text": "GemNet-OC", "start": 54, "end": 63}, {"text": "graph neural network", "start": 158, "end": 178}]}, "relations": {"subclass of": [{"head": {"text": "GemNet-OC", "start": 54, "end": 63}, "tail": {"text": "graph neural network", "start": 158, "end": 178}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We then train the GNN building block encoder using a contrastive loss to map small geometric variations of the same building block to similar latent vectors in the embedding space .", "output": {"entities": {"method": [{"text": "GNN building block encoder", "start": 18, "end": 44}, {"text": "contrastive loss", "start": 53, "end": 69}]}, "relations": {"part of": [{"head": {"text": "contrastive loss", "start": 53, "end": 69}, "tail": {"text": "GNN building block encoder", "start": 18, "end": 44}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The projected embedding is obtained by projecting the building block embedding b i using a multi-layer perceptron ( MLP ) projection head : s i , j = ⋯ ⋯ ⋯ ⋯ ⋯ p i = MLP ( b i ) .", "output": {"entities": {"method": [{"text": "multi-layer perceptron", "start": 91, "end": 113}, {"text": "MLP", "start": 116, "end": 119}, {"text": "MLP", "start": 166, "end": 169}]}, "relations": {"synonym of": [{"head": {"text": "MLP", "start": 116, "end": 119}, "tail": {"text": "multi-layer perceptron", "start": 91, "end": 113}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The MOFDiff model is composed of four components ( Figure 1 ( a ) ) : ( 1 ) A periodic GemNet-OC encoder5 that outputs a latent vector z = PGNN E ( M C ) ; ( 2 ) an MLP predictor that predicts the lattice parameters and the number of building blocks from the latent code z : L , K = MLP L , K ( z ) ; ( 3 ) a periodic GemNet-OC denoiser that outputs SE ( 3 ) - equivariant scores to denoise random structures to CG MOF structures conditional on the latent code : s A C , s X C = PGNN D ( M C t , z ) , where s A C , s X C are the predicted scores for building block identities A C and coordinates X C , and M C t is a noisy CG structure at time t in the diffusion process ; ( 4 ) an MLP predictor that predicts properties c ( such as CO 2 working capacity ) from z : ĉ = MLP P ( z ) .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 4, "end": 11}, {"text": "GemNet-OC", "start": 87, "end": 96}, {"text": "MLP", "start": 165, "end": 168}, {"text": "MLP", "start": 283, "end": 286}, {"text": "GemNet-OC", "start": 318, "end": 327}, {"text": "MLP", "start": 683, "end": 686}, {"text": "MLP", "start": 771, "end": 774}]}, "relations": {"part of": [{"head": {"text": "GemNet-OC", "start": 87, "end": 96}, "tail": {"text": "MOFDiff", "start": 4, "end": 11}}, {"head": {"text": "MLP", "start": 165, "end": 168}, "tail": {"text": "MOFDiff", "start": 4, "end": 11}}, {"head": {"text": "GemNet-OC", "start": 318, "end": 327}, "tail": {"text": "MOFDiff", "start": 4, "end": 11}}, {"head": {"text": "MLP", "start": 683, "end": 686}, "tail": {"text": "MOFDiff", "start": 4, "end": 11}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We design an assembly algorithm that optimizes the building block orientations to match the connection points of adjacent building blocks such that the MOF becomes connected ( visualized in Figure 4 ) .", "output": {"entities": {"method": [{"text": "assembly algorithm", "start": 13, "end": 31}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This optimization algorithm places Gaussian densities at the position of each connection point and maximizes the overlap of these densities between compatible connection points .", "output": {"entities": {"method": [{"text": "optimization algorithm", "start": 5, "end": 27}, {"text": "Gaussian densities", "start": 35, "end": 53}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "The radius of the Gaussian densities is gradually reduced in the optimization process : at the beginning , the radius is high , so the optimization problem is smoother , and it is simpler to find an approximate solution .", "output": {"entities": {"method": [{"text": "Gaussian densities", "start": 18, "end": 36}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Details regarding the assembly algorithm are included in Appendix A .2 .", "output": {"entities": {"method": [{"text": "assembly algorithm", "start": 22, "end": 40}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our experiments aim to evaluate two capabilities of MOFDiff : 1 .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 52, "end": 59}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Can MOFDiff design functional MOF structures optimized for carbon capture ?", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 4, "end": 11}], "task": [{"text": "carbon capture", "start": 59, "end": 73}]}, "relations": {"used for": [{"head": {"text": "MOFDiff", "start": 4, "end": 11}, "tail": {"text": "carbon capture", "start": 59, "end": 73}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We train and evaluate our method on the BW-DB dataset , which contains 304k MOFs with less than 20 building blocks ( as defined by the metal-oxo decomposition algorithm ) from the 324k MOFs in Boyd et al . 2019 .", "output": {"entities": {"dataset": [{"text": "BW-DB", "start": 40, "end": 45}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We use 289k MOFs ( 95% ) for training and the rest for validation .", "output": {"entities": {"method": [{"text": "MOFs", "start": 12, "end": 16}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We do not keep a test split , as we evaluate our generative model on random sampling and inverse design capabilities .", "output": {"entities": {"method": [{"text": "generative model", "start": 49, "end": 65}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For the relaxed structure , we adopt MOFChecker [ Jablonka , 2023 ] to check validity .", "output": {"entities": {"method": [{"text": "MOFChecker", "start": 37, "end": 47}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFChecker includes a variety of criteria : the presence of metal and organic elements , porosity , no overlapping atoms , no non-physical atomic valences or coordination environments , no atoms or molecules disconnected from the primary MOF structure , and no excessively large atomic charges .", "output": {"entities": {"method": [{"text": "MOFChecker", "start": 0, "end": 10}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For novelty , we adopt the MOF identifier extracted by MOFid and say a MOF is novel if its MOFid differs from any other MOFs in the training dataset .", "output": {"entities": {"method": [{"text": "MOFid", "start": 55, "end": 60}, {"text": "MOFid", "start": 91, "end": 96}, {"text": "MOFs", "start": 120, "end": 124}]}, "relations": {"compare with": [{"head": {"text": "MOFid", "start": 91, "end": 96}, "tail": {"text": "MOFs", "start": 120, "end": 124}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We also count the number of unique generations by filtering out replicate samples using their MOFid .", "output": {"entities": {"method": [{"text": "MOFid", "start": 94, "end": 99}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff generates valid and novel MOFs .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We randomly sample 10,000 latent vectors from N ( 0 , I ) , decode through MOFDiff , assemble , and apply force field relaxation to obtain the atomic structures .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 75, "end": 82}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "For faithful evaluation , we carry out grand canonical Monte Carlo ( GCMC ) simulations to calculate the gas adsorption properties of MOF structures .", "output": {"entities": {"method": [{"text": "grand canonical Monte Carlo", "start": 39, "end": 66}, {"text": "GCMC", "start": 69, "end": 73}]}, "relations": {"synonym of": [{"head": {"text": "GCMC", "start": 69, "end": 73}, "tail": {"text": "grand canonical Monte Carlo", "start": 39, "end": 66}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "In the appendix , Figure 11 shows the benchmark results of our implementation compared to the original labels of BW-DB , which demonstrate a strong positive correlation with our implementation underestimating the original labels by an average of around 30% .", "output": {"entities": {"dataset": [{"text": "BW-DB", "start": 113, "end": 118}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff is trained over the original BW-DB labels and uses latent-space optimization to maximize the BW-DB property values .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}], "dataset": [{"text": "BW-DB", "start": 37, "end": 42}, {"text": "BW-DB", "start": 101, "end": 106}]}, "relations": {"trained with": [{"head": {"text": "MOFDiff", "start": 0, "end": 7}, "tail": {"text": "BW-DB", "start": 37, "end": 42}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff discovers promising candidates for carbon capture .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}], "task": [{"text": "carbon capture", "start": 43, "end": 57}]}, "relations": {"used for": [{"head": {"text": "MOFDiff", "start": 0, "end": 7}, "tail": {"text": "carbon capture", "start": 43, "end": 57}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "After conducting the validity checks described in Section 4.1 , we find 2054 MOFs that are valid , novel , and unique ( Figure 8 ( a ) ) .", "output": {"entities": {"method": [{"text": "MOFs", "start": 77, "end": 81}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These 2054 MOFs are then simulated with our GCMC workflow to compute gas adsorption properties .", "output": {"entities": {"method": [{"text": "MOFs", "start": 11, "end": 15}, {"text": "GCMC", "start": 44, "end": 48}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Given the systematic differences between the original labels of BW-DB and those calculated with our reimplemented GCMC workflow , we randomly sampled 5,000 MOFs from the BW-DB dataset and recalculated the gas adsorption properties using our GCMC workflow to provide a fair baseline for comparison .", "output": {"entities": {"dataset": [{"text": "BW-DB", "start": 64, "end": 69}, {"text": "BW-DB", "start": 170, "end": 175}], "method": [{"text": "GCMC", "start": 114, "end": 118}, {"text": "GCMC", "start": 241, "end": 245}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We observe that MOFDiff From an efficiency perspective , GCMC simulations take orders of magnitude more computational time ( tens of minutes to hours ) than other components of the MOF design pipeline ( seconds to tens of seconds ) .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 16, "end": 23}, {"text": "GCMC", "start": 57, "end": 61}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "These simulations can also be made more accurate at significantly higher computational costs ( days ) by converging sampling to tighter confidence intervals or using more advanced techniques , such as including blocking spheres , which prohibit Monte Carlo insertion of gas molecules into kinetically prohibited pores of the MOF , and calculating atomic charges with density functional theory ( DFT ) .", "output": {"entities": {"method": [{"text": "density functional theory", "start": 367, "end": 392}, {"text": "DFT", "start": 395, "end": 398}]}, "relations": {"synonym of": [{"head": {"text": "DFT", "start": 395, "end": 398}, "tail": {"text": "density functional theory", "start": 367, "end": 392}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Therefore , the efficiency of a MOF design pipeline can be evaluated by the average number of GCMC simulations required to find one qualifying MOF for carbon capture applications .", "output": {"entities": {"method": [{"text": "GCMC", "start": 94, "end": 98}], "task": [{"text": "carbon capture", "start": 151, "end": 165}]}, "relations": {"used for": [{"head": {"text": "GCMC", "start": 94, "end": 98}, "tail": {"text": "carbon capture", "start": 151, "end": 165}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Naively sampling from the BW-DB dataset requires , on average , 58.1 GCMC simulations to find one MOF with a working capacity of more than 2 mol / kg .", "output": {"entities": {"dataset": [{"text": "BW-DB", "start": 26, "end": 31}], "method": [{"text": "GCMC", "start": 69, "end": 73}]}, "relations": {"evaluated with": [{"head": {"text": "GCMC", "start": 69, "end": 73}, "tail": {"text": "BW-DB", "start": 26, "end": 31}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Beyond efficiency , MOFDiff's generation flexibility also allows it to discover top MOF candidates that are outstanding for carbon capture .", "output": {"entities": {"method": [{"text": "MOFDiff's", "start": 20, "end": 29}], "task": [{"text": "carbon capture", "start": 124, "end": 138}]}, "relations": {"used for": [{"head": {"text": "MOFDiff's", "start": 20, "end": 29}, "tail": {"text": "carbon capture", "start": 124, "end": 138}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We compute gas adsorption properties of 18 MOFs that have been investigated for CO 2 adsorption from previous literature [ Madden et al . , 2017 , Coelho et al . , 2016 , González-Zamora and Ibarra , 2017 , Boyd et al . , 2019 ] using our GCMC simulation workflow .", "output": {"entities": {"method": [{"text": "MOFs", "start": 43, "end": 47}, {"text": "GCMC", "start": 239, "end": 243}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff can discover highly promising candidates , making up 9 out of the top 10 MOFs .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}, {"text": "MOFs", "start": 81, "end": 85}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "This comparison confirms MOFDiff's capability in advancing functional MOF design .", "output": {"entities": {"method": [{"text": "MOFDiff's", "start": 25, "end": 34}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We proposed MOFDiff , a coarse-grained diffusion model for metal-organic framework design .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 12, "end": 19}, {"text": "coarse-grained diffusion model", "start": 24, "end": 54}], "task": [{"text": "metal-organic framework design", "start": 59, "end": 89}]}, "relations": {"subclass of": [{"head": {"text": "MOFDiff", "start": 12, "end": 19}, "tail": {"text": "coarse-grained diffusion model", "start": 24, "end": 54}}], "used for": [{"head": {"text": "MOFDiff", "start": 12, "end": 19}, "tail": {"text": "metal-organic framework design", "start": 59, "end": 89}}, {"head": {"text": "coarse-grained diffusion model", "start": 24, "end": 54}, "tail": {"text": "metal-organic framework design", "start": 59, "end": 89}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "Our work presents a complete pipeline of representation , generative model , structural relaxation , and molecular simulation to address a specific carbon capture materials design problem .", "output": {"entities": {"method": [{"text": "representation", "start": 41, "end": 55}, {"text": "generative model", "start": 58, "end": 74}, {"text": "structural relaxation", "start": 77, "end": 98}, {"text": "molecular simulation", "start": 105, "end": 125}], "task": [{"text": "carbon capture materials design", "start": 148, "end": 179}]}, "relations": {"used for": [{"head": {"text": "representation", "start": 41, "end": 55}, "tail": {"text": "carbon capture materials design", "start": 148, "end": 179}}, {"head": {"text": "generative model", "start": 58, "end": 74}, "tail": {"text": "carbon capture materials design", "start": 148, "end": 179}}, {"head": {"text": "structural relaxation", "start": 77, "end": 98}, "tail": {"text": "carbon capture materials design", "start": 148, "end": 179}}, {"head": {"text": "molecular simulation", "start": 105, "end": 125}, "tail": {"text": "carbon capture materials design", "start": 148, "end": 179}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "We then design an assembly algorithm to realize the all-atom MOF structures and characterize their properties with molecular simulations .", "output": {"entities": {"method": [{"text": "assembly algorithm", "start": 18, "end": 36}]}, "relations": {}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}
{"input": "MOFDiff can generate valid and novel MOF structures covering a wide range of structural properties as well as optimize MOFs for carbon capture applications that surpass state-of-the-art MOFs in molecular simulations .", "output": {"entities": {"method": [{"text": "MOFDiff", "start": 0, "end": 7}, {"text": "MOFs", "start": 119, "end": 123}, {"text": "MOFs", "start": 186, "end": 190}], "task": [{"text": "carbon capture", "start": 128, "end": 142}, {"text": "molecular simulations", "start": 194, "end": 215}]}, "relations": {"used for": [{"head": {"text": "MOFDiff", "start": 0, "end": 7}, "tail": {"text": "MOFs", "start": 119, "end": 123}}, {"head": {"text": "MOFDiff", "start": 0, "end": 7}, "tail": {"text": "carbon capture", "start": 128, "end": 142}}, {"head": {"text": "MOFs", "start": 119, "end": 123}, "tail": {"text": "carbon capture", "start": 128, "end": 142}}, {"head": {"text": "MOFs", "start": 186, "end": 190}, "tail": {"text": "molecular simulations", "start": 194, "end": 215}}]}}, "schema": {"entities": [{"label": "dataset", "description": "A realistic collection of data that is used for training, validating, or testing the algorithms. These datasets can consist of various forms of data such as text, images, videos, or structured data. For example, MNIST, COCO, AGNews, IMDb, etc.", "description_source": "official"}, {"label": "method", "description": "The approach, algorithm, or technique used to solve a specific task/problem. Methods encompass the computational algorithms, model architectures, and the training procedures that are employed to make predictions or decisions based on data. For example, Convolutional Neural Networks (CNNs).", "description_source": "official"}, {"label": "task", "description": "The specific problem or type of problem that a ML/AI model is designed to solve. Tasks can be broad, like classification, regression, or clustering, or they can be very specific, such as Pedestrian Detection, Autonomous Driving, Sentiment Analysis, Named Entity Recognition and Relation Extraction.", "description_source": "official"}], "relations": [{"label": "part of", "description": "The head method is a component or a part of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subclass of", "description": "The head method is a subclass or a specialized version of the tail method (Method-Method).", "description_source": "official", "symmetric": false}, {"label": "subtask of", "description": "The head task is a subset or a specific aspect of the broader tail task (Task-Task).", "description_source": "official", "symmetric": false}, {"label": "benchmark for", "description": "The head dataset serves as a standard or benchmark for evaluating the performance of methods on the tail task (Dataset-Task).", "description_source": "official", "symmetric": false}, {"label": "trained with", "description": "The head method is trained using the tail dataset (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "evaluated with", "description": "The head method is evaluated using the tail dataset to test its performance or conduct the experiments (Method-Dataset).", "description_source": "official", "symmetric": false}, {"label": "synonym of", "description": "Symmetric relation: the two terms or entities are considered to have the same or very similar meaning, such as an abbreviation.", "description_source": "official", "symmetric": true}, {"label": "used for", "description": "The head is utilized for achieving or performing the tail. A flexible relation between entities of any type.", "description_source": "official", "symmetric": false}, {"label": "compare with", "description": "Symmetric relation: one entity is compared with another to highlight differences, similarities, or both.", "description_source": "official", "symmetric": true}], "setting": "end_to_end"}}