{"input": "The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary .", "output": {"relations": {"conjunction": [{"head": {"text": "nouns", "start": 45, "end": 50}, "tail": {"text": "reflexive pronouns", "start": 55, "end": 73}}], "feature of": [{"head": {"text": "grammatical gender", "start": 167, "end": 185}, "tail": {"text": "languages", "start": 189, "end": 198}}], "hyponym of": [{"head": {"text": "French", "start": 207, "end": 213}, "tail": {"text": "languages", "start": 189, "end": 198}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , a novel method to learn the intrinsic object structure for robust visual tracking is proposed .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 24, "end": 30}, "tail": {"text": "intrinsic object structure", "start": 44, "end": 70}}, {"head": {"text": "intrinsic object structure", "start": 44, "end": 70}, "tail": {"text": "robust visual tracking", "start": 75, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data .", "output": {"relations": {"feature of": [{"head": {"text": "low dimensional manifold", "start": 70, "end": 94}, "tail": {"text": "parameterized object state", "start": 33, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on this assumption , firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation , the obtained non-rigid part of object state reduces even to 2 dimensions .", "output": {"relations": {"used for": [{"head": {"text": "dimensionality reduction and density estimation algorithm", "start": 50, "end": 107}, "tail": {"text": "unsupervised learning of object intrinsic representation", "start": 112, "end": 168}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Secondly the dynamical model is derived and trained based on this intrinsic representation .", "output": {"relations": {"used for": [{"head": {"text": "intrinsic representation", "start": 66, "end": 90}, "tail": {"text": "dynamical model", "start": 13, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker .", "output": {"relations": {"part of": [{"head": {"text": "intrinsic object structure", "start": 20, "end": 46}, "tail": {"text": "particle-filter style tracker", "start": 68, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable .", "output": {"relations": {"used for": [{"head": {"text": "dynamical model", "start": 124, "end": 139}, "tail": {"text": "particle-filter style tracker", "start": 146, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion .", "output": {"relations": {"compare": [{"head": {"text": "tracker", "start": 34, "end": 41}, "tail": {"text": "trackers", "start": 77, "end": 85}}], "used for": [{"head": {"text": "tracker", "start": 34, "end": 41}, "tail": {"text": "tracking of complex non-rigid motions", "start": 93, "end": 130}}, {"head": {"text": "trackers", "start": 77, "end": 85}, "tail": {"text": "tracking of complex non-rigid motions", "start": 93, "end": 130}}], "hyponym of": [{"head": {"text": "fish twisting", "start": 139, "end": 152}, "tail": {"text": "complex non-rigid motions", "start": 105, "end": 130}}], "feature of": [{"head": {"text": "self-occlusion", "start": 158, "end": 172}, "tail": {"text": "fish twisting", "start": 139, "end": 152}}, {"head": {"text": "inter-frame lip motion", "start": 183, "end": 205}, "tail": {"text": "fish twisting", "start": 139, "end": 152}}], "conjunction": [{"head": {"text": "self-occlusion", "start": 158, "end": 172}, "tail": {"text": "inter-frame lip motion", "start": 183, "end": 205}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed method also has the potential to solve other type of tracking problems .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "tracking problems", "start": 66, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for silent speech enhancement and electrolaryngeal speech enhancement .", "output": {"relations": {"used for": [{"head": {"text": "digital signal processor -LRB- DSP -RRB- implementation", "start": 29, "end": 84}, "tail": {"text": "real-time statistical voice conversion -LRB- VC -RRB-", "start": 88, "end": 141}}, {"head": {"text": "real-time statistical voice conversion -LRB- VC -RRB-", "start": 88, "end": 141}, "tail": {"text": "silent speech enhancement", "start": 146, "end": 171}}, {"head": {"text": "real-time statistical voice conversion -LRB- VC -RRB-", "start": 88, "end": 141}, "tail": {"text": "electrolaryngeal speech enhancement", "start": 176, "end": 211}}], "conjunction": [{"head": {"text": "silent speech enhancement", "start": 146, "end": 171}, "tail": {"text": "electrolaryngeal speech enhancement", "start": 176, "end": 211}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees .", "output": {"relations": {"hyponym of": [{"head": {"text": "Electrolaryngeal speech", "start": 0, "end": 23}, "tail": {"text": "alaryngeal speech", "start": 55, "end": 72}}], "used for": [{"head": {"text": "speaking method", "start": 100, "end": 115}, "tail": {"text": "alaryngeal speech", "start": 55, "end": 72}}, {"head": {"text": "speaking method", "start": 100, "end": 115}, "tail": {"text": "laryngectomees", "start": 120, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness .", "output": {"relations": {"evaluate for": [{"head": {"text": "sound quality", "start": 14, "end": 27}, "tail": {"text": "NAM and electrolaryngeal speech", "start": 31, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "VC has proven to be one of the promising approaches to address this problem , and it has been successfully implemented on devices with sufficient computational resources .", "output": {"relations": {"used for": [{"head": {"text": "devices", "start": 122, "end": 129}, "tail": {"text": "it", "start": 82, "end": 84}}], "feature of": [{"head": {"text": "sufficient computational resources", "start": 135, "end": 169}, "tail": {"text": "devices", "start": 122, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use .", "output": {"relations": {"feature of": [{"head": {"text": "limited computational resources", "start": 63, "end": 94}, "tail": {"text": "devices", "start": 21, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we further implement real-time VC on a DSP .", "output": {"relations": {"used for": [{"head": {"text": "DSP", "start": 53, "end": 56}, "tail": {"text": "real-time VC", "start": 35, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .", "output": {"relations": {"used for": [{"head": {"text": "real-time VC", "start": 57, "end": 69}, "tail": {"text": "speech enhancement systems", "start": 21, "end": 47}}], "hyponym of": [{"head": {"text": "one", "start": 72, "end": 75}, "tail": {"text": "speech enhancement systems", "start": 21, "end": 47}}, {"head": {"text": "other", "start": 114, "end": 119}, "tail": {"text": "speech enhancement systems", "start": 21, "end": 47}}], "conjunction": [{"head": {"text": "one", "start": 72, "end": 75}, "tail": {"text": "other", "start": 114, "end": 119}}, {"head": {"text": "computational cost", "start": 210, "end": 228}, "tail": {"text": "conversion accuracy", "start": 246, "end": 265}}], "evaluate for": [{"head": {"text": "computational cost", "start": 210, "end": 228}, "tail": {"text": "methods", "start": 189, "end": 196}}, {"head": {"text": "conversion accuracy", "start": 246, "end": 265}, "tail": {"text": "methods", "start": 189, "end": 196}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation .", "output": {"relations": {"used for": [{"head": {"text": "DSP", "start": 90, "end": 93}, "tail": {"text": "real-time VC", "start": 50, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "paraphrase", "start": 49, "end": 59}}, {"head": {"text": "paraphrase", "start": 49, "end": 59}, "tail": {"text": "machine translation evaluation measures", "start": 127, "end": 166}}], "hyponym of": [{"head": {"text": "BLEU", "start": 172, "end": 176}, "tail": {"text": "machine translation evaluation measures", "start": 127, "end": 166}}, {"head": {"text": "NIST", "start": 181, "end": 185}, "tail": {"text": "machine translation evaluation measures", "start": 127, "end": 166}}], "conjunction": [{"head": {"text": "BLEU", "start": 172, "end": 176}, "tail": {"text": "NIST", "start": 181, "end": 185}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .", "output": {"relations": {"conjunction": [{"head": {"text": "equivalence in meaning", "start": 170, "end": 192}, "tail": {"text": "grammaticality", "start": 98, "end": 112}}, {"head": {"text": "meaning equivalence", "start": 239, "end": 258}, "tail": {"text": "entailment", "start": 262, "end": 272}}, {"head": {"text": "internal lexical and syntactical variation", "start": 311, "end": 353}, "tail": {"text": "equivalence in meaning", "start": 170, "end": 192}}], "used for": [{"head": {"text": "meaning equivalence", "start": 239, "end": 258}, "tail": {"text": "paraphrases", "start": 31, "end": 42}}, {"head": {"text": "entailment", "start": 262, "end": 272}, "tail": {"text": "paraphrases", "start": 31, "end": 42}}], "compare": [{"head": {"text": "paraphrases", "start": 31, "end": 42}, "tail": {"text": "hand-produced sets", "start": 409, "end": 427}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 37, "end": 43}, "tail": {"text": "paraphrase", "start": 4, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Graph unification remains the most expensive part of unification-based grammar parsing .", "output": {"relations": {"part of": [{"head": {"text": "Graph unification", "start": 0, "end": 17}, "tail": {"text": "unification-based grammar parsing", "start": 53, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We focus on one speed-up element in the design of unification algorithms : avoidance of copying of unmodified subgraphs .", "output": {"relations": {"part of": [{"head": {"text": "speed-up element", "start": 16, "end": 32}, "tail": {"text": "unification algorithms", "start": 50, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a method of attaining such a design through a method of structure-sharing which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers .", "output": {"relations": {"used for": [{"head": {"text": "structure-sharing", "start": 67, "end": 84}, "tail": {"text": "method", "start": 13, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions .", "output": {"relations": {"used for": [{"head": {"text": "scheme", "start": 13, "end": 19}, "tail": {"text": "cyclic structures", "start": 183, "end": 200}}], "feature of": [{"head": {"text": "quasi-destructive scheme 's ability", "start": 71, "end": 106}, "tail": {"text": "scheme", "start": 13, "end": 19}}], "conjunction": [{"head": {"text": "over copying", "start": 116, "end": 128}, "tail": {"text": "early copying", "start": 133, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 46, "end": 52}, "tail": {"text": "subcategorization dictionary", "start": 72, "end": 100}}, {"head": {"text": "textual corpora", "start": 106, "end": 121}, "tail": {"text": "system", "start": 46, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 71, "end": 77}, "tail": {"text": "subcategorization dictionary", "start": 27, "end": 55}}], "evaluate for": [{"head": {"text": "accuracy", "start": 91, "end": 99}, "tail": {"text": "parser", "start": 105, "end": 111}}, {"head": {"text": "parser", "start": 105, "end": 111}, "tail": {"text": "subcategorization dictionary", "start": 27, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A number of powerful registration criteria have been developed in the last decade , most prominently the criterion of maximum mutual information .", "output": {"relations": {"hyponym of": [{"head": {"text": "maximum mutual information", "start": 118, "end": 144}, "tail": {"text": "registration criteria", "start": 21, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although this criterion provides for good registration results in many applications , it remains a purely low-level criterion .", "output": {"relations": {"feature of": [{"head": {"text": "low-level criterion", "start": 106, "end": 125}, "tail": {"text": "it", "start": 16, "end": 18}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian framework", "start": 34, "end": 52}, "tail": {"text": "image registration methods", "start": 157, "end": 183}}, {"head": {"text": "statistically learned prior knowledge", "start": 75, "end": 112}, "tail": {"text": "image registration methods", "start": 157, "end": 183}}], "feature of": [{"head": {"text": "joint intensity distribution", "start": 123, "end": 151}, "tail": {"text": "statistically learned prior knowledge", "start": 75, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs .", "output": {"relations": {"used for": [{"head": {"text": "kernel density estimate", "start": 24, "end": 47}, "tail": {"text": "prior", "start": 4, "end": 9}}, {"head": {"text": "kernel density estimate", "start": 24, "end": 47}, "tail": {"text": "joint intensity distributions", "start": 64, "end": 93}}, {"head": {"text": "pre-registered image pairs", "start": 132, "end": 158}, "tail": {"text": "joint intensity distributions", "start": 64, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions .", "output": {"relations": {"used for": [{"head": {"text": "registration process", "start": 52, "end": 72}, "tail": {"text": "missing low-level information", "start": 91, "end": 120}}, {"head": {"text": "it", "start": 124, "end": 126}, "tail": {"text": "intensity correspondences", "start": 134, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a method for synthesizing complex , photo-realistic facade images , from a single example .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "synthesizing complex , photo-realistic facade images", "start": 24, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After parsing the example image into its semantic components , a tiling for it is generated .", "output": {"relations": {"used for": [{"head": {"text": "tiling", "start": 65, "end": 71}, "tail": {"text": "semantic components", "start": 41, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Novel tilings can then be created , yielding facade textures with different dimensions or with occluded parts inpainted .", "output": {"relations": {"feature of": [{"head": {"text": "occluded parts inpainted", "start": 95, "end": 119}, "tail": {"text": "facade textures", "start": 45, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A genetic algorithm guides the novel facades as well as inpainted parts to be consistent with the example , both in terms of their overall structure and their detailed textures .", "output": {"relations": {"used for": [{"head": {"text": "genetic algorithm", "start": 2, "end": 19}, "tail": {"text": "facades", "start": 37, "end": 44}}, {"head": {"text": "genetic algorithm", "start": 2, "end": 19}, "tail": {"text": "inpainted parts", "start": 56, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method .", "output": {"relations": {"evaluate for": [{"head": {"text": "multiple standard datasets", "start": 22, "end": 48}, "tail": {"text": "method", "start": 149, "end": 155}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce a new interactive corpus exploration tool called InfoMagnets .", "output": {"relations": {"hyponym of": [{"head": {"text": "InfoMagnets", "start": 62, "end": 73}, "tail": {"text": "interactive corpus exploration tool", "start": 19, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining .", "output": {"relations": {"used for": [{"head": {"text": "InfoMagnets", "start": 0, "end": 11}, "tail": {"text": "exploratory corpus analysis", "start": 27, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 15, "end": 17}, "tail": {"text": "domains", "start": 180, "end": 187}}], "hyponym of": [{"head": {"text": "tutorial dialogue", "start": 190, "end": 207}, "tail": {"text": "domains", "start": 180, "end": 187}}, {"head": {"text": "on-line communities", "start": 249, "end": 268}, "tail": {"text": "domains", "start": 180, "end": 187}}], "conjunction": [{"head": {"text": "tutorial dialogue", "start": 190, "end": 207}, "tail": {"text": "on-line communities", "start": 249, "end": 268}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "As an educational tool , it has been used as part of a unit on protocol analysis in an Educational Research Methods course .", "output": {"relations": {"used for": [{"head": {"text": "educational tool", "start": 6, "end": 22}, "tail": {"text": "protocol analysis", "start": 63, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Sources of training data suitable for language modeling of conversational speech are limited .", "output": {"relations": {"used for": [{"head": {"text": "conversational speech", "start": 59, "end": 80}, "tail": {"text": "language modeling", "start": 38, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task , but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams .", "output": {"relations": {"used for": [{"head": {"text": "class-dependent interpolation of N-grams", "start": 243, "end": 283}, "tail": {"text": "recognition task", "start": 140, "end": 156}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a method for detecting 3D objects using multi-modalities .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "detecting 3D objects", "start": 24, "end": 44}}, {"head": {"text": "multi-modalities", "start": 51, "end": 67}, "tail": {"text": "method", "start": 13, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While it is generic , we demonstrate it on the combination of an image and a dense depth map which give complementary object information .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 6, "end": 8}, "tail": {"text": "it", "start": 6, "end": 8}}, {"head": {"text": "image", "start": 65, "end": 70}, "tail": {"text": "it", "start": 6, "end": 8}}, {"head": {"text": "dense depth map", "start": 77, "end": 92}, "tail": {"text": "it", "start": 6, "end": 8}}], "conjunction": [{"head": {"text": "image", "start": 65, "end": 70}, "tail": {"text": "dense depth map", "start": 77, "end": 92}}], "feature of": [{"head": {"text": "complementary object information", "start": 104, "end": 136}, "tail": {"text": "dense depth map", "start": 77, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities .", "output": {"relations": {"used for": [{"head": {"text": "templates", "start": 46, "end": 55}, "tail": {"text": "modalities", "start": 83, "end": 93}}, {"head": {"text": "approach", "start": 159, "end": 167}, "tail": {"text": "single modalities", "start": 222, "end": 239}}, {"head": {"text": "state-of-the-art methods", "start": 194, "end": 218}, "tail": {"text": "single modalities", "start": 222, "end": 239}}], "compare": [{"head": {"text": "approach", "start": 159, "end": 167}, "tail": {"text": "state-of-the-art methods", "start": 194, "end": 218}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .", "output": {"relations": {"used for": [{"head": {"text": "compact description of a video sequence", "start": 4, "end": 43}, "tail": {"text": "domains", "start": 121, "end": 128}}, {"head": {"text": "image map", "start": 61, "end": 70}, "tail": {"text": "compact description of a video sequence", "start": 4, "end": 43}}, {"head": {"text": "dominant motion", "start": 77, "end": 92}, "tail": {"text": "compact description of a video sequence", "start": 4, "end": 43}}], "conjunction": [{"head": {"text": "image map", "start": 61, "end": 70}, "tail": {"text": "dominant motion", "start": 77, "end": 92}}, {"head": {"text": "video browsing and retrieval", "start": 141, "end": 169}, "tail": {"text": "compression", "start": 172, "end": 183}}, {"head": {"text": "compression", "start": 172, "end": 183}, "tail": {"text": "mosaicing", "start": 186, "end": 195}}, {"head": {"text": "mosaicing", "start": 186, "end": 195}, "tail": {"text": "visual summarization", "start": 202, "end": 222}}], "hyponym of": [{"head": {"text": "video browsing and retrieval", "start": 141, "end": 169}, "tail": {"text": "domains", "start": 121, "end": 128}}, {"head": {"text": "compression", "start": 172, "end": 183}, "tail": {"text": "domains", "start": 121, "end": 128}}, {"head": {"text": "mosaicing", "start": 186, "end": 195}, "tail": {"text": "domains", "start": 121, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a task which has been , in the past , addressed through temporally localized motion estimates .", "output": {"relations": {"used for": [{"head": {"text": "localized motion estimates", "start": 200, "end": 226}, "tail": {"text": "task", "start": 135, "end": 139}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To avoid this oscillation , we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations , leading to more meaningful content summarization .", "output": {"relations": {"used for": [{"head": {"text": "generic temporal constraint", "start": 63, "end": 90}, "tail": {"text": "motion model", "start": 43, "end": 55}}, {"head": {"text": "generic temporal constraint", "start": 63, "end": 90}, "tail": {"text": "content summarization", "start": 185, "end": 206}}], "evaluate for": [{"head": {"text": "robustness", "start": 111, "end": 121}, "tail": {"text": "generic temporal constraint", "start": 63, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various resolutions and changing illuminations .", "output": {"relations": {"part of": [{"head": {"text": "dominant factors", "start": 113, "end": 129}, "tail": {"text": "domain divergence", "start": 72, "end": 89}}], "hyponym of": [{"head": {"text": "viewpoints", "start": 149, "end": 159}, "tail": {"text": "dominant factors", "start": 113, "end": 129}}, {"head": {"text": "resolutions", "start": 170, "end": 181}, "tail": {"text": "dominant factors", "start": 113, "end": 129}}], "conjunction": [{"head": {"text": "viewpoints", "start": 149, "end": 159}, "tail": {"text": "resolutions", "start": 170, "end": 181}}, {"head": {"text": "resolutions", "start": 170, "end": 181}, "tail": {"text": "illuminations", "start": 195, "end": 208}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Fortunately , an intermediate domain could often be found to build a bridge across them to facilitate the learning problem .", "output": {"relations": {"used for": [{"head": {"text": "intermediate domain", "start": 17, "end": 36}, "tail": {"text": "learning problem", "start": 106, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem .", "output": {"relations": {"used for": [{"head": {"text": "Coupled Marginalized Denoising Auto-encoders framework", "start": 29, "end": 83}, "tail": {"text": "cross-domain problem", "start": 99, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , we design two marginalized denoising auto-encoders , one for the target and the other for source as well as the intermediate one .", "output": {"relations": {"hyponym of": [{"head": {"text": "one", "start": 68, "end": 71}, "tail": {"text": "marginalized denoising auto-encoders", "start": 29, "end": 65}}, {"head": {"text": "other", "start": 95, "end": 100}, "tail": {"text": "marginalized denoising auto-encoders", "start": 29, "end": 65}}], "conjunction": [{"head": {"text": "one", "start": 68, "end": 71}, "tail": {"text": "other", "start": 95, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To better couple the two denoising auto-encoders learning , we incorporate a feature mapping , which tends to transfer knowledge between the intermediate domain and the target one .", "output": {"relations": {"part of": [{"head": {"text": "feature mapping", "start": 77, "end": 92}, "tail": {"text": "denoising auto-encoders learning", "start": 25, "end": 57}}], "used for": [{"head": {"text": "feature mapping", "start": 77, "end": 92}, "tail": {"text": "intermediate domain", "start": 141, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , the maximum margin criterion , e.g. , intra-class com-pactness and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .", "output": {"relations": {"hyponym of": [{"head": {"text": "intra-class com-pactness", "start": 52, "end": 76}, "tail": {"text": "maximum margin criterion", "start": 18, "end": 42}}, {"head": {"text": "inter-class penalty", "start": 81, "end": 100}, "tail": {"text": "maximum margin criterion", "start": 18, "end": 42}}], "conjunction": [{"head": {"text": "intra-class com-pactness", "start": 52, "end": 76}, "tail": {"text": "inter-class penalty", "start": 81, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods .", "output": {"relations": {"evaluate for": [{"head": {"text": "tasks", "start": 29, "end": 34}, "tail": {"text": "method", "start": 76, "end": 82}}], "compare": [{"head": {"text": "method", "start": 76, "end": 82}, "tail": {"text": "state-of-the-art methods", "start": 92, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process .", "output": {"relations": {"part of": [{"head": {"text": "dictionary bases", "start": 77, "end": 93}, "tail": {"text": "age-group specific dictionaries", "start": 21, "end": 52}}], "used for": [{"head": {"text": "linear combination", "start": 233, "end": 251}, "tail": {"text": "personalized aging process", "start": 293, "end": 319}}, {"head": {"text": "patterns", "start": 261, "end": 269}, "tail": {"text": "linear combination", "start": 233, "end": 251}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , beyond the aging dictionaries , each subject may have extra personalized facial characteristics , e.g. mole , which are invariant in the aging process .", "output": {"relations": {"hyponym of": [{"head": {"text": "mole", "start": 111, "end": 115}, "tail": {"text": "personalized facial characteristics", "start": 68, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups .", "output": {"relations": {"used for": [{"head": {"text": "personality-aware coupled reconstruction loss", "start": 7, "end": 52}, "tail": {"text": "dictionaries", "start": 78, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .", "output": {"relations": {"compare": [{"head": {"text": "solution", "start": 70, "end": 78}, "tail": {"text": "state-of-the-arts", "start": 90, "end": 107}}], "used for": [{"head": {"text": "solution", "start": 70, "end": 78}, "tail": {"text": "personalized aging progression", "start": 119, "end": 149}}, {"head": {"text": "state-of-the-arts", "start": 90, "end": 107}, "tail": {"text": "personalized aging progression", "start": 119, "end": 149}}, {"head": {"text": "synthesizing aging faces", "start": 219, "end": 243}, "tail": {"text": "cross-age face verification", "start": 188, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a draft scheme of the model formalizing the structure of communicative context in dialogue interaction .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 33, "end": 38}, "tail": {"text": "structure of communicative context", "start": 55, "end": 89}}], "feature of": [{"head": {"text": "dialogue interaction", "start": 93, "end": 113}, "tail": {"text": "structure of communicative context", "start": 55, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by web interfaces optimized for desktop PCs .", "output": {"relations": {"used for": [{"head": {"text": "web interfaces", "start": 100, "end": 114}, "tail": {"text": "desktop PCs", "start": 129, "end": 140}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we develop an algorithm , MINPATH , that automatically improves wireless web navigation by suggesting useful shortcut links in real time .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 28, "end": 37}, "tail": {"text": "wireless web navigation", "start": 78, "end": 101}}, {"head": {"text": "algorithm", "start": 28, "end": 37}, "tail": {"text": "wireless web navigation", "start": 78, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 43, "end": 48}, "tail": {"text": "MINPATH", "start": 0, "end": 7}}, {"head": {"text": "model", "start": 43, "end": 48}, "tail": {"text": "web visitor behavior", "start": 52, "end": 72}}, {"head": {"text": "model", "start": 43, "end": 48}, "tail": {"text": "savings of shortcut links", "start": 89, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We explore a variety of predictive models , including Na ¨ ıve Bayes mixture models and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .", "output": {"relations": {"hyponym of": [{"head": {"text": "Na ¨ ıve Bayes mixture models", "start": 54, "end": 83}, "tail": {"text": "predictive models", "start": 24, "end": 41}}, {"head": {"text": "mixtures of Markov models", "start": 88, "end": 113}, "tail": {"text": "predictive models", "start": 24, "end": 41}}], "conjunction": [{"head": {"text": "Na ¨ ıve Bayes mixture models", "start": 54, "end": 83}, "tail": {"text": "mixtures of Markov models", "start": 88, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a particular approach to parsing that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 34, "end": 42}, "tail": {"text": "parsing", "start": 46, "end": 53}}, {"head": {"text": "unification-based parsing", "start": 87, "end": 112}, "tail": {"text": "approach", "start": 34, "end": 42}}, {"head": {"text": "classification-based knowledge representation", "start": 120, "end": 165}, "tail": {"text": "approach", "start": 34, "end": 42}}], "conjunction": [{"head": {"text": "classification-based knowledge representation", "start": 120, "end": 165}, "tail": {"text": "unification-based parsing", "start": 87, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .", "output": {"relations": {"used for": [{"head": {"text": "unification-based grammatical frameworks", "start": 3, "end": 43}, "tail": {"text": "linguistic information", "start": 90, "end": 112}}, {"head": {"text": "KL-ONE-like knowledge representation systems", "start": 186, "end": 230}, "tail": {"text": "they", "start": 115, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This commonality suggests that some of the classification-based representation techniques can be applied to unification-based linguistic descriptions .", "output": {"relations": {"used for": [{"head": {"text": "classification-based representation techniques", "start": 43, "end": 89}, "tail": {"text": "unification-based linguistic descriptions", "start": 108, "end": 149}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This merging supports the integration of semantic and syntactic information into the same system , simultaneously subject to the same types of processes , in an efficient manner .", "output": {"relations": {"used for": [{"head": {"text": "semantic and syntactic information", "start": 41, "end": 75}, "tail": {"text": "system", "start": 90, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .", "output": {"relations": {"used for": [{"head": {"text": "KL-ONE style representation", "start": 13, "end": 40}, "tail": {"text": "parsing", "start": 45, "end": 52}}, {"head": {"text": "KL-ONE style representation", "start": 13, "end": 40}, "tail": {"text": "semantic interpretation", "start": 57, "end": 80}}, {"head": {"text": "PSI-KLONE system", "start": 107, "end": 123}, "tail": {"text": "KL-ONE style representation", "start": 13, "end": 40}}, {"head": {"text": "incremental description refinement", "start": 205, "end": 239}, "tail": {"text": "parsing", "start": 45, "end": 52}}], "conjunction": [{"head": {"text": "parsing", "start": 45, "end": 52}, "tail": {"text": "semantic interpretation", "start": 57, "end": 80}}], "hyponym of": [{"head": {"text": "incremental description refinement", "start": 205, "end": 239}, "tail": {"text": "inference process", "start": 180, "end": 197}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a language tutoring application for deaf learners of written English .", "output": {"relations": {"used for": [{"head": {"text": "user knowledge modeling architecture", "start": 36, "end": 72}, "tail": {"text": "ICICLE system", "start": 81, "end": 94}}, {"head": {"text": "language tutoring application", "start": 99, "end": 128}, "tail": {"text": "deaf learners", "start": 133, "end": 146}}, {"head": {"text": "written English", "start": 150, "end": 165}, "tail": {"text": "language tutoring application", "start": 99, "end": 128}}], "hyponym of": [{"head": {"text": "ICICLE system", "start": 81, "end": 94}, "tail": {"text": "language tutoring application", "start": 99, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model will represent the language proficiency of the user and is designed to be referenced during both writing analysis and feedback production .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "writing analysis", "start": 107, "end": 123}}, {"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "feedback production", "start": 128, "end": 147}}], "conjunction": [{"head": {"text": "writing analysis", "start": 107, "end": 123}, "tail": {"text": "feedback production", "start": 128, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We motivate our model design by citing relevant research on second language and cognitive skill acquisition , and briefly discuss preliminary empirical evidence supporting the design .", "output": {"relations": {"used for": [{"head": {"text": "second language and cognitive skill acquisition", "start": 60, "end": 107}, "tail": {"text": "model design", "start": 16, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of granularity and specificity .", "output": {"relations": {"used for": [{"head": {"text": "design", "start": 31, "end": 37}, "tail": {"text": "language assessment / correction application", "start": 90, "end": 134}}, {"head": {"text": "design", "start": 31, "end": 37}, "tail": {"text": "user proficiency", "start": 147, "end": 163}}], "evaluate for": [{"head": {"text": "granularity", "start": 183, "end": 194}, "tail": {"text": "user proficiency", "start": 147, "end": 163}}, {"head": {"text": "specificity", "start": 199, "end": 210}, "tail": {"text": "user proficiency", "start": 147, "end": 163}}], "conjunction": [{"head": {"text": "granularity", "start": 183, "end": 194}, "tail": {"text": "specificity", "start": 199, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Constraint propagation is one of the key techniques in constraint programming , and a large body of work has built up around it .", "output": {"relations": {"part of": [{"head": {"text": "Constraint propagation", "start": 0, "end": 22}, "tail": {"text": "constraint programming", "start": 55, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we present SHORTSTR2 , a development of the Simple Tabular Reduction algorithm STR2 + .", "output": {"relations": {"used for": [{"head": {"text": "Simple Tabular Reduction algorithm STR2 +", "start": 58, "end": 99}, "tail": {"text": "SHORTSTR2", "start": 25, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports , while being much simpler .", "output": {"relations": {"compare": [{"head": {"text": "SHORTSTR2", "start": 13, "end": 22}, "tail": {"text": "SHORTGAC", "start": 67, "end": 75}}, {"head": {"text": "SHORTSTR2", "start": 13, "end": 22}, "tail": {"text": "HAGGISGAC", "start": 80, "end": 89}}], "conjunction": [{"head": {"text": "SHORTGAC", "start": 67, "end": 75}, "tail": {"text": "HAGGISGAC", "start": 80, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "When a constraint is amenable to short supports , the short support set can be exponentially smaller than the full-length support set .", "output": {"relations": {"compare": [{"head": {"text": "short support set", "start": 54, "end": 71}, "tail": {"text": "full-length support set", "start": 110, "end": 133}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .", "output": {"relations": {"used for": [{"head": {"text": "SHORTSTR2", "start": 18, "end": 27}, "tail": {"text": "short supports", "start": 80, "end": 94}}, {"head": {"text": "SHORTSTR2", "start": 18, "end": 27}, "tail": {"text": "drop-in replacement", "start": 145, "end": 164}}, {"head": {"text": "algorithm", "start": 58, "end": 67}, "tail": {"text": "short supports", "start": 80, "end": 94}}, {"head": {"text": "full-length supports", "start": 100, "end": 120}, "tail": {"text": "SHORTSTR2", "start": 18, "end": 27}}, {"head": {"text": "full-length supports", "start": 100, "end": 120}, "tail": {"text": "SHORTSTR2", "start": 18, "end": 27}}, {"head": {"text": "full-length supports", "start": 100, "end": 120}, "tail": {"text": "algorithm", "start": 58, "end": 67}}, {"head": {"text": "full-length supports", "start": 100, "end": 120}, "tail": {"text": "algorithm", "start": 58, "end": 67}}, {"head": {"text": "drop-in replacement", "start": 145, "end": 164}, "tail": {"text": "STR2 +", "start": 169, "end": 175}}], "conjunction": [{"head": {"text": "algorithm", "start": 58, "end": 67}, "tail": {"text": "SHORTSTR2", "start": 18, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a detection method for orthographic variants caused by transliteration in a large corpus .", "output": {"relations": {"used for": [{"head": {"text": "detection method", "start": 13, "end": 29}, "tail": {"text": "orthographic variants", "start": 34, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method employs two similarities .", "output": {"relations": {"used for": [{"head": {"text": "similarities", "start": 23, "end": 35}, "tail": {"text": "method", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One is string similarity based on edit distance .", "output": {"relations": {"used for": [{"head": {"text": "edit distance", "start": 34, "end": 47}, "tail": {"text": "string similarity", "start": 7, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The other is contextual similarity by a vector space model .", "output": {"relations": {"used for": [{"head": {"text": "vector space model", "start": 40, "end": 58}, "tail": {"text": "contextual similarity", "start": 13, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results show that the method performed a 0.889 F-measure in an open test .", "output": {"relations": {"evaluate for": [{"head": {"text": "F-measure", "start": 60, "end": 69}, "tail": {"text": "method", "start": 35, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Uncertainty handling plays an important role during shape tracking .", "output": {"relations": {"used for": [{"head": {"text": "Uncertainty handling", "start": 0, "end": 20}, "tail": {"text": "shape tracking", "start": 52, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .", "output": {"relations": {"used for": [{"head": {"text": "fusion of measurement information with system dynamics and shape priors", "start": 32, "end": 103}, "tail": {"text": "tracking", "start": 125, "end": 133}}, {"head": {"text": "tracking", "start": 125, "end": 133}, "tail": {"text": "noisy images", "start": 155, "end": 167}}], "hyponym of": [{"head": {"text": "ultrasound sequences", "start": 176, "end": 196}, "tail": {"text": "noisy images", "start": 155, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Nevertheless , this approach required user initialization of the tracking process .", "output": {"relations": {"used for": [{"head": {"text": "user initialization", "start": 38, "end": 57}, "tail": {"text": "approach", "start": 20, "end": 28}}, {"head": {"text": "user initialization", "start": 38, "end": 57}, "tail": {"text": "tracking process", "start": 65, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework .", "output": {"relations": {"used for": [{"head": {"text": "boosted shape detection", "start": 70, "end": 93}, "tail": {"text": "automatic initial-ization problem", "start": 22, "end": 55}}, {"head": {"text": "generic measurement process", "start": 99, "end": 126}, "tail": {"text": "boosted shape detection", "start": 70, "end": 93}}], "part of": [{"head": {"text": "it", "start": 34, "end": 36}, "tail": {"text": "tracking framework", "start": 153, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "As a result , we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood .", "output": {"relations": {"used for": [{"head": {"text": "maximum likelihood", "start": 126, "end": 144}, "tail": {"text": "posterior shape model", "start": 82, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 4, "end": 13}, "tail": {"text": "automatic tracking of endocardium", "start": 33, "end": 66}}], "part of": [{"head": {"text": "endocardium", "start": 55, "end": 66}, "tail": {"text": "ultrasound sequences of the human heart", "start": 70, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations .", "output": {"relations": {"conjunction": [{"head": {"text": "detection", "start": 9, "end": 18}, "tail": {"text": "tracking", "start": 30, "end": 38}}, {"head": {"text": "approaches", "start": 86, "end": 96}, "tail": {"text": "inter-expert variations", "start": 101, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a syntax-based constraint for word alignment , known as the cohesion constraint .", "output": {"relations": {"used for": [{"head": {"text": "syntax-based constraint", "start": 13, "end": 36}, "tail": {"text": "word alignment", "start": 41, "end": 55}}], "hyponym of": [{"head": {"text": "cohesion constraint", "start": 71, "end": 90}, "tail": {"text": "syntax-based constraint", "start": 13, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence .", "output": {"relations": {"used for": [{"head": {"text": "English phrases", "start": 21, "end": 36}, "tail": {"text": "It", "start": 0, "end": 2}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the utility of this constraint in two different algorithms .", "output": {"relations": {"evaluate for": [{"head": {"text": "algorithms", "start": 60, "end": 70}, "tail": {"text": "constraint", "start": 32, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results show that it can provide a significant improvement in alignment quality .", "output": {"relations": {"evaluate for": [{"head": {"text": "alignment quality", "start": 66, "end": 83}, "tail": {"text": "it", "start": 22, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text .", "output": {"relations": {"used for": [{"head": {"text": "Centering Theory", "start": 81, "end": 97}, "tail": {"text": "entity-based representation of discourse", "start": 19, "end": 59}}, {"head": {"text": "raw text", "start": 137, "end": 145}, "tail": {"text": "entity-based representation of discourse", "start": 19, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function .", "output": {"relations": {"used for": [{"head": {"text": "ranking learning problem", "start": 34, "end": 58}, "tail": {"text": "coherence assessment", "start": 8, "end": 28}}, {"head": {"text": "discourse representation", "start": 86, "end": 110}, "tail": {"text": "ranking function", "start": 148, "end": 164}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model .", "output": {"relations": {"compare": [{"head": {"text": "induced model", "start": 37, "end": 50}, "tail": {"text": "coherence model", "start": 114, "end": 129}}], "evaluate for": [{"head": {"text": "accuracy", "start": 81, "end": 89}, "tail": {"text": "induced model", "start": 37, "end": 50}}, {"head": {"text": "accuracy", "start": 81, "end": 89}, "tail": {"text": "coherence model", "start": 114, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper introduces a robust interactive method for speech understanding .", "output": {"relations": {"used for": [{"head": {"text": "robust interactive method", "start": 24, "end": 49}, "tail": {"text": "speech understanding", "start": 54, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The generalized LR parsing is enhanced in this approach .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 47, "end": 55}, "tail": {"text": "generalized LR parsing", "start": 4, "end": 26}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "When a very noisy portion is detected , the parser skips that portion using a fake non-terminal symbol .", "output": {"relations": {"used for": [{"head": {"text": "non-terminal symbol", "start": 83, "end": 102}, "tail": {"text": "parser", "start": 44, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This method is also capable of handling unknown words , which is important in practical systems .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 5, "end": 11}, "tail": {"text": "unknown words", "start": 40, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper shows that it is very often possible to identify the source language of medium-length speeches in the EUROPARL corpus on the basis of frequency counts of word n-grams -LRB- 87.2 % -96.7 % accuracy depending on classification method -RRB- .", "output": {"relations": {"part of": [{"head": {"text": "medium-length speeches", "start": 83, "end": 105}, "tail": {"text": "EUROPARL corpus", "start": 113, "end": 128}}], "evaluate for": [{"head": {"text": "accuracy", "start": 199, "end": 207}, "tail": {"text": "classification method", "start": 221, "end": 242}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We investigated whether automatic phonetic transcriptions -LRB- APTs -RRB- can replace manually verified phonetic transcriptions -LRB- MPTs -RRB- in a large corpus-based study on pronunciation variation .", "output": {"relations": {"compare": [{"head": {"text": "automatic phonetic transcriptions -LRB- APTs -RRB-", "start": 24, "end": 74}, "tail": {"text": "manually verified phonetic transcriptions", "start": 87, "end": 128}}], "used for": [{"head": {"text": "automatic phonetic transcriptions -LRB- APTs -RRB-", "start": 24, "end": 74}, "tail": {"text": "pronunciation variation", "start": 179, "end": 202}}, {"head": {"text": "manually verified phonetic transcriptions", "start": 87, "end": 128}, "tail": {"text": "pronunciation variation", "start": 179, "end": 202}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We trained classifiers on the speech processes extracted from the alignments of an APT and an MPT with a canonical transcription .", "output": {"relations": {"used for": [{"head": {"text": "speech processes", "start": 30, "end": 46}, "tail": {"text": "classifiers", "start": 11, "end": 22}}, {"head": {"text": "alignments", "start": 66, "end": 76}, "tail": {"text": "speech processes", "start": 30, "end": 46}}, {"head": {"text": "alignments", "start": 66, "end": 76}, "tail": {"text": "APT", "start": 83, "end": 86}}, {"head": {"text": "alignments", "start": 66, "end": 76}, "tail": {"text": "MPT", "start": 94, "end": 97}}, {"head": {"text": "canonical transcription", "start": 105, "end": 128}, "tail": {"text": "alignments", "start": 66, "end": 76}}], "conjunction": [{"head": {"text": "APT", "start": 83, "end": 86}, "tail": {"text": "MPT", "start": 94, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We tested whether the classifiers were equally good at verifying whether unknown transcriptions represent read speech or telephone dialogues , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .", "output": {"relations": {"used for": [{"head": {"text": "classifiers", "start": 22, "end": 33}, "tail": {"text": "unknown transcriptions", "start": 73, "end": 95}}, {"head": {"text": "unknown transcriptions", "start": 73, "end": 95}, "tail": {"text": "read speech", "start": 106, "end": 117}}, {"head": {"text": "unknown transcriptions", "start": 73, "end": 95}, "tail": {"text": "telephone dialogues", "start": 121, "end": 140}}], "conjunction": [{"head": {"text": "read speech", "start": 106, "end": 117}, "tail": {"text": "telephone dialogues", "start": 121, "end": 140}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our results not only show that similar distinguishing speech processes were identified ; our APT-based classifier yielded better classification accuracy than the MPT-based classifier whilst using fewer classification features .", "output": {"relations": {"compare": [{"head": {"text": "APT-based classifier", "start": 93, "end": 113}, "tail": {"text": "MPT-based classifier", "start": 162, "end": 182}}], "evaluate for": [{"head": {"text": "classification accuracy", "start": 129, "end": 152}, "tail": {"text": "APT-based classifier", "start": 93, "end": 113}}, {"head": {"text": "classification accuracy", "start": 129, "end": 152}, "tail": {"text": "MPT-based classifier", "start": 162, "end": 182}}], "used for": [{"head": {"text": "classification features", "start": 202, "end": 225}, "tail": {"text": "APT-based classifier", "start": 93, "end": 113}}, {"head": {"text": "classification features", "start": 202, "end": 225}, "tail": {"text": "MPT-based classifier", "start": 162, "end": 182}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Machine reading is a relatively new field that features computer programs designed to read flowing text and extract fact assertions expressed by the narrative content .", "output": {"relations": {"used for": [{"head": {"text": "computer programs", "start": 56, "end": 73}, "tail": {"text": "flowing text", "start": 91, "end": 103}}, {"head": {"text": "computer programs", "start": 56, "end": 73}, "tail": {"text": "fact assertions", "start": 116, "end": 131}}], "feature of": [{"head": {"text": "fact assertions", "start": 116, "end": 131}, "tail": {"text": "narrative content", "start": 149, "end": 166}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This task involves two core technologies : natural language processing -LRB- NLP -RRB- and information extraction -LRB- IE -RRB- .", "output": {"relations": {"part of": [{"head": {"text": "natural language processing -LRB- NLP -RRB-", "start": 43, "end": 86}, "tail": {"text": "task", "start": 5, "end": 9}}, {"head": {"text": "information extraction -LRB- IE -RRB-", "start": 91, "end": 128}, "tail": {"text": "task", "start": 5, "end": 9}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we describe a machine reading system that we have developed within a cognitive architecture .", "output": {"relations": {"feature of": [{"head": {"text": "cognitive architecture", "start": 83, "end": 105}, "tail": {"text": "machine reading system", "start": 28, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how we have integrated into the framework several levels of knowledge for a particular domain , ideas from cognitive semantics and construction grammar , plus tools from prior NLP and IE research .", "output": {"relations": {"conjunction": [{"head": {"text": "cognitive semantics", "start": 115, "end": 134}, "tail": {"text": "construction grammar", "start": 139, "end": 159}}, {"head": {"text": "prior NLP", "start": 178, "end": 187}, "tail": {"text": "IE research", "start": 192, "end": 203}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The result is a system that is capable of reading and interpreting complex and fairly idiosyncratic texts in the family history domain .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 16, "end": 22}, "tail": {"text": "idiosyncratic texts", "start": 86, "end": 105}}], "feature of": [{"head": {"text": "family history domain", "start": 113, "end": 134}, "tail": {"text": "idiosyncratic texts", "start": 86, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present two methods for capturing nonstationary chaos , then present a few examples including biological signals , ocean waves and traffic flow .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 15, "end": 22}, "tail": {"text": "nonstationary chaos", "start": 37, "end": 56}}], "hyponym of": [{"head": {"text": "biological signals", "start": 97, "end": 115}, "tail": {"text": "examples", "start": 78, "end": 86}}, {"head": {"text": "ocean waves", "start": 118, "end": 129}, "tail": {"text": "examples", "start": 78, "end": 86}}, {"head": {"text": "traffic flow", "start": 134, "end": 146}, "tail": {"text": "examples", "start": 78, "end": 86}}], "conjunction": [{"head": {"text": "biological signals", "start": 97, "end": 115}, "tail": {"text": "ocean waves", "start": 118, "end": 129}}, {"head": {"text": "ocean waves", "start": 118, "end": 129}, "tail": {"text": "traffic flow", "start": 134, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a formal analysis for a large class of words called alternative markers , which includes other -LRB- than -RRB- , such -LRB- as -RRB- , and besides .", "output": {"relations": {"used for": [{"head": {"text": "formal analysis", "start": 22, "end": 37}, "tail": {"text": "alternative markers", "start": 72, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These words appear frequently enough in dialog to warrant serious attention , yet present natural language search engines perform poorly on queries containing them .", "output": {"relations": {"part of": [{"head": {"text": "words", "start": 6, "end": 11}, "tail": {"text": "dialog", "start": 40, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine 's operational semantics .", "output": {"relations": {"part of": [{"head": {"text": "approximation of the formal analysis", "start": 96, "end": 132}, "tail": {"text": "search engine", "start": 33, "end": 46}}, {"head": {"text": "operational semantics", "start": 178, "end": 199}, "tail": {"text": "search engine", "start": 33, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The value of this approach is that as the operational semantics of natural language applications improve , even larger improvements are possible .", "output": {"relations": {"part of": [{"head": {"text": "operational semantics", "start": 42, "end": 63}, "tail": {"text": "natural language applications", "start": 67, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We find that simple interpolation methods , like log-linear and linear interpolation , improve the performance but fall short of the performance of an oracle .", "output": {"relations": {"hyponym of": [{"head": {"text": "log-linear and linear interpolation", "start": 49, "end": 84}, "tail": {"text": "interpolation methods", "start": 20, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Actually , the oracle acts like a dynamic combiner with hard decisions using the reference .", "output": {"relations": {"feature of": [{"head": {"text": "hard decisions", "start": 56, "end": 70}, "tail": {"text": "dynamic combiner", "start": 34, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree .", "output": {"relations": {"used for": [{"head": {"text": "neural network", "start": 67, "end": 81}, "tail": {"text": "method", "start": 13, "end": 19}}, {"head": {"text": "decision tree", "start": 87, "end": 100}, "tail": {"text": "method", "start": 13, "end": 19}}], "conjunction": [{"head": {"text": "decision tree", "start": 87, "end": 100}, "tail": {"text": "neural network", "start": 67, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "LMs", "start": 30, "end": 33}}, {"head": {"text": "confidence measures", "start": 39, "end": 58}, "tail": {"text": "method", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a new method for the representation of NLP structures within reranking approaches .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 18, "end": 24}, "tail": {"text": "NLP structures", "start": 51, "end": 65}}], "feature of": [{"head": {"text": "reranking approaches", "start": 73, "end": 93}, "tail": {"text": "NLP structures", "start": 51, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We make use of a conditional log-linear model , with hidden variables representing the assignment of lexical items to word clusters or word senses .", "output": {"relations": {"used for": [{"head": {"text": "hidden variables", "start": 53, "end": 69}, "tail": {"text": "conditional log-linear model", "start": 17, "end": 45}}], "conjunction": [{"head": {"text": "word clusters", "start": 118, "end": 131}, "tail": {"text": "word senses", "start": 135, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model learns to automatically make these assignments based on a discriminative training criterion .", "output": {"relations": {"used for": [{"head": {"text": "discriminative training criterion", "start": 68, "end": 101}, "tail": {"text": "model", "start": 4, "end": 9}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Training and decoding with the model requires summing over an exponential number of hidden-variable assignments : the required summations can be computed efficiently and exactly using dynamic programming .", "output": {"relations": {"used for": [{"head": {"text": "dynamic programming", "start": 184, "end": 203}, "tail": {"text": "summations", "start": 127, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "As a case study , we apply the model to parse reranking .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 31, "end": 36}, "tail": {"text": "parse reranking", "start": 40, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model gives an F-measure improvement of ~ 1.25 % beyond the base parser , and an ~ 0.25 % improvement beyond Collins -LRB- 2000 -RRB- reranker .", "output": {"relations": {"compare": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "base parser", "start": 64, "end": 75}}, {"head": {"text": "base parser", "start": 64, "end": 75}, "tail": {"text": "Collins -LRB- 2000 -RRB- reranker", "start": 113, "end": 146}}], "evaluate for": [{"head": {"text": "F-measure", "start": 19, "end": 28}, "tail": {"text": "model", "start": 4, "end": 9}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although our experiments are focused on parsing , the techniques described generalize naturally to NLP structures other than parse trees .", "output": {"relations": {"used for": [{"head": {"text": "techniques", "start": 54, "end": 64}, "tail": {"text": "parsing", "start": 40, "end": 47}}, {"head": {"text": "techniques", "start": 54, "end": 64}, "tail": {"text": "NLP structures", "start": 99, "end": 113}}, {"head": {"text": "techniques", "start": 54, "end": 64}, "tail": {"text": "parse trees", "start": 125, "end": 136}}], "conjunction": [{"head": {"text": "parse trees", "start": 125, "end": 136}, "tail": {"text": "NLP structures", "start": 99, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an algorithm for learning the time-varying shape of a non-rigid 3D object from uncalibrated 2D tracking data .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 23, "end": 32}, "tail": {"text": "learning the time-varying shape of a non-rigid 3D object", "start": 37, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We constrain the problem by assuming that the object shape at each time instant is drawn from a Gaussian distribution .", "output": {"relations": {"used for": [{"head": {"text": "Gaussian distribution", "start": 96, "end": 117}, "tail": {"text": "object shape", "start": 46, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on this assumption , the algorithm simultaneously estimates 3D shape and motion for each time frame , learns the parameters of the Gaussian , and robustly fills-in missing data points .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 31, "end": 40}, "tail": {"text": "3D shape and motion", "start": 66, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We then extend the algorithm to model temporal smoothness in object shape , thus allowing it to handle severe cases of missing data .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 19, "end": 28}, "tail": {"text": "temporal smoothness in object shape", "start": 38, "end": 73}}, {"head": {"text": "it", "start": 24, "end": 26}, "tail": {"text": "missing data", "start": 119, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Automatic summarization and information extraction are two important Internet services .", "output": {"relations": {"conjunction": [{"head": {"text": "Automatic summarization", "start": 0, "end": 23}, "tail": {"text": "information extraction", "start": 28, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "MUC and SUMMAC play their appropriate roles in the next generation Internet .", "output": {"relations": {"conjunction": [{"head": {"text": "MUC", "start": 0, "end": 3}, "tail": {"text": "SUMMAC", "start": 8, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1 .", "output": {"relations": {"used for": [{"head": {"text": "models", "start": 77, "end": 83}, "tail": {"text": "summary generation", "start": 109, "end": 127}}, {"head": {"text": "models", "start": 77, "end": 83}, "tail": {"text": "tasks", "start": 138, "end": 143}}], "part of": [{"head": {"text": "tasks", "start": 138, "end": 143}, "tail": {"text": "SUMMAC-1", "start": 157, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For categorization task , positive feature vectors and negative feature vectors are used cooperatively to construct generic , indicative summaries .", "output": {"relations": {"used for": [{"head": {"text": "positive feature vectors", "start": 26, "end": 50}, "tail": {"text": "categorization task", "start": 4, "end": 23}}, {"head": {"text": "positive feature vectors", "start": 26, "end": 50}, "tail": {"text": "generic , indicative summaries", "start": 116, "end": 146}}, {"head": {"text": "negative feature vectors", "start": 55, "end": 79}, "tail": {"text": "categorization task", "start": 4, "end": 23}}, {"head": {"text": "negative feature vectors", "start": 55, "end": 79}, "tail": {"text": "generic , indicative summaries", "start": 116, "end": 146}}], "conjunction": [{"head": {"text": "positive feature vectors", "start": 26, "end": 50}, "tail": {"text": "negative feature vectors", "start": 55, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For adhoc task , a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment , to rank relevant sentences , and to generate the user-directed summaries .", "output": {"relations": {"used for": [{"head": {"text": "text model", "start": 19, "end": 29}, "tail": {"text": "adhoc task", "start": 4, "end": 14}}, {"head": {"text": "text model", "start": 19, "end": 29}, "tail": {"text": "discourse segment", "start": 109, "end": 126}}, {"head": {"text": "text model", "start": 19, "end": 29}, "tail": {"text": "user-directed summaries", "start": 178, "end": 201}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0 .", "output": {"relations": {"evaluate for": [{"head": {"text": "NormF", "start": 26, "end": 31}, "tail": {"text": "adhoc tasks", "start": 86, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023 .", "output": {"relations": {"evaluate for": [{"head": {"text": "NormF", "start": 4, "end": 9}, "tail": {"text": "categorization task", "start": 64, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our system outperforms the average system in categorization task but does a common job in adhoc task .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "system", "start": 4, "end": 10}}], "evaluate for": [{"head": {"text": "categorization task", "start": 45, "end": 64}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "categorization task", "start": 45, "end": 64}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "adhoc task", "start": 90, "end": 100}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "adhoc task", "start": 90, "end": 100}, "tail": {"text": "categorization task", "start": 45, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In real-world action recognition problems , low-level features can not adequately characterize the rich spatial-temporal structures in action videos .", "output": {"relations": {"feature of": [{"head": {"text": "rich spatial-temporal structures", "start": 99, "end": 131}, "tail": {"text": "action videos", "start": 135, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The second type is data-driven attributes , which are learned from data using dictionary learning methods .", "output": {"relations": {"used for": [{"head": {"text": "dictionary learning methods", "start": 78, "end": 105}, "tail": {"text": "data-driven attributes", "start": 19, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a discriminative and compact attribute-based representation by selecting a subset of discriminative attributes from a large attribute set .", "output": {"relations": {"used for": [{"head": {"text": "discriminative attributes", "start": 96, "end": 121}, "tail": {"text": "discriminative and compact attribute-based representation", "start": 13, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Three attribute selection criteria are proposed and formulated as a submodular optimization problem .", "output": {"relations": {"used for": [{"head": {"text": "submodular optimization problem", "start": 68, "end": 99}, "tail": {"text": "attribute selection criteria", "start": 6, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed attribute-based representation can significantly boost the performance of action recognition algorithms and outperform most recently proposed recognition approaches .", "output": {"relations": {"evaluate for": [{"head": {"text": "Olympic Sports and UCF101 datasets", "start": 28, "end": 62}, "tail": {"text": "attribute-based representation", "start": 93, "end": 123}}], "used for": [{"head": {"text": "attribute-based representation", "start": 93, "end": 123}, "tail": {"text": "action recognition algorithms", "start": 167, "end": 196}}], "compare": [{"head": {"text": "action recognition algorithms", "start": 167, "end": 196}, "tail": {"text": "recognition approaches", "start": 235, "end": 257}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Landsbergen 's advocacy of analytical inverses for compositional syntax rules encourages the application of Definite Clause Grammar techniques to the construction of a parser returning Montague analysis trees .", "output": {"relations": {"used for": [{"head": {"text": "analytical inverses", "start": 27, "end": 46}, "tail": {"text": "compositional syntax rules", "start": 51, "end": 77}}, {"head": {"text": "analytical inverses", "start": 27, "end": 46}, "tail": {"text": "Definite Clause Grammar techniques", "start": 108, "end": 142}}, {"head": {"text": "Definite Clause Grammar techniques", "start": 108, "end": 142}, "tail": {"text": "parser returning Montague analysis trees", "start": 168, "end": 208}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A parser MDCC is presented which implements an augmented Friedman - Warren algorithm permitting post referencing * and interfaces with a language of intenslonal logic translator LILT so as to display the derivational history of corresponding reduced IL formulae .", "output": {"relations": {"used for": [{"head": {"text": "augmented Friedman - Warren algorithm", "start": 47, "end": 84}, "tail": {"text": "parser MDCC", "start": 2, "end": 13}}, {"head": {"text": "derivational history", "start": 204, "end": 224}, "tail": {"text": "intenslonal logic translator LILT", "start": 149, "end": 182}}], "feature of": [{"head": {"text": "post referencing", "start": 96, "end": 112}, "tail": {"text": "augmented Friedman - Warren algorithm", "start": 47, "end": 84}}, {"head": {"text": "reduced IL formulae", "start": 242, "end": 261}, "tail": {"text": "derivational history", "start": 204, "end": 224}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Some familiarity with Montague 's PTQ and the basic DCG mechanism is assumed .", "output": {"relations": {"conjunction": [{"head": {"text": "Montague 's PTQ", "start": 22, "end": 37}, "tail": {"text": "basic DCG mechanism", "start": 46, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Stochastic attention-based models have been shown to improve computational efficiency at test time , but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates .", "output": {"relations": {"evaluate for": [{"head": {"text": "computational efficiency", "start": 61, "end": 85}, "tail": {"text": "Stochastic attention-based models", "start": 0, "end": 33}}], "conjunction": [{"head": {"text": "intractable posterior inference", "start": 147, "end": 178}, "tail": {"text": "stochastic gradient estimates", "start": 204, "end": 233}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Borrowing techniques from the literature on training deep generative models , we present the Wake-Sleep Recurrent Attention Model , a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients .", "output": {"relations": {"used for": [{"head": {"text": "Borrowing techniques", "start": 0, "end": 20}, "tail": {"text": "deep generative models", "start": 53, "end": 75}}, {"head": {"text": "method", "start": 134, "end": 140}, "tail": {"text": "stochastic attention networks", "start": 154, "end": 183}}, {"head": {"text": "stochastic attention networks", "start": 154, "end": 183}, "tail": {"text": "posterior inference", "start": 199, "end": 218}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation .", "output": {"relations": {"evaluate for": [{"head": {"text": "training time", "start": 49, "end": 62}, "tail": {"text": "method", "start": 17, "end": 23}}, {"head": {"text": "image classification", "start": 115, "end": 135}, "tail": {"text": "method", "start": 17, "end": 23}}, {"head": {"text": "caption generation", "start": 140, "end": 158}, "tail": {"text": "method", "start": 17, "end": 23}}], "feature of": [{"head": {"text": "training time", "start": 49, "end": 62}, "tail": {"text": "stochastic attention networks", "start": 67, "end": 96}}], "conjunction": [{"head": {"text": "image classification", "start": 115, "end": 135}, "tail": {"text": "caption generation", "start": 140, "end": 158}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A new exemplar-based framework unifying image completion , texture synthesis and image inpainting is presented in this work .", "output": {"relations": {"used for": [{"head": {"text": "exemplar-based framework", "start": 6, "end": 30}, "tail": {"text": "image completion", "start": 40, "end": 56}}, {"head": {"text": "exemplar-based framework", "start": 6, "end": 30}, "tail": {"text": "texture synthesis", "start": 59, "end": 76}}, {"head": {"text": "exemplar-based framework", "start": 6, "end": 30}, "tail": {"text": "image inpainting", "start": 81, "end": 97}}], "conjunction": [{"head": {"text": "image completion", "start": 40, "end": 56}, "tail": {"text": "texture synthesis", "start": 59, "end": 76}}, {"head": {"text": "texture synthesis", "start": 59, "end": 76}, "tail": {"text": "image inpainting", "start": 81, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Contrary to existing greedy techniques , these tasks are posed in the form of a discrete global optimization problem with a well defined objective function .", "output": {"relations": {"compare": [{"head": {"text": "greedy techniques", "start": 21, "end": 38}, "tail": {"text": "tasks", "start": 47, "end": 52}}], "feature of": [{"head": {"text": "discrete global optimization problem", "start": 80, "end": 116}, "tail": {"text": "tasks", "start": 47, "end": 52}}, {"head": {"text": "well defined objective function", "start": 124, "end": 155}, "tail": {"text": "discrete global optimization problem", "start": 80, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important extensions over standard belief propagation -LRB- BP -RRB- : '' priority-based message scheduling '' and '' dynamic label pruning '' .", "output": {"relations": {"used for": [{"head": {"text": "optimization scheme", "start": 33, "end": 52}, "tail": {"text": "problem", "start": 17, "end": 24}}, {"head": {"text": "belief propagation -LRB- BP -RRB-", "start": 146, "end": 179}, "tail": {"text": "extensions", "start": 121, "end": 131}}], "hyponym of": [{"head": {"text": "Priority-BP", "start": 62, "end": 73}, "tail": {"text": "optimization scheme", "start": 33, "end": 52}}, {"head": {"text": "priority-based message scheduling", "start": 185, "end": 218}, "tail": {"text": "extensions", "start": 121, "end": 131}}, {"head": {"text": "dynamic label pruning", "start": 229, "end": 250}, "tail": {"text": "extensions", "start": 121, "end": 131}}], "part of": [{"head": {"text": "extensions", "start": 121, "end": 131}, "tail": {"text": "optimization scheme", "start": 33, "end": 52}}], "conjunction": [{"head": {"text": "priority-based message scheduling", "start": 185, "end": 218}, "tail": {"text": "dynamic label pruning", "start": 229, "end": 250}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels .", "output": {"relations": {"used for": [{"head": {"text": "extensions", "start": 10, "end": 20}, "tail": {"text": "intolerable computational cost of BP", "start": 58, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Moreover , both extensions are generic and can therefore be applied to any MRF energy function as well .", "output": {"relations": {"used for": [{"head": {"text": "extensions", "start": 16, "end": 26}, "tail": {"text": "MRF energy function", "start": 75, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The effectiveness of our method is demonstrated on a wide variety of image completion examples .", "output": {"relations": {"used for": [{"head": {"text": "image completion examples", "start": 69, "end": 94}, "tail": {"text": "method", "start": 25, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we compare the relative effects of segment order , segmentation and segment contiguity on the retrieval performance of a translation memory system .", "output": {"relations": {"conjunction": [{"head": {"text": "segment order", "start": 51, "end": 64}, "tail": {"text": "segmentation", "start": 67, "end": 79}}, {"head": {"text": "segmentation", "start": 67, "end": 79}, "tail": {"text": "segment contiguity", "start": 84, "end": 102}}], "used for": [{"head": {"text": "segment order", "start": 51, "end": 64}, "tail": {"text": "translation memory system", "start": 137, "end": 162}}, {"head": {"text": "segmentation", "start": 67, "end": 79}, "tail": {"text": "translation memory system", "start": 137, "end": 162}}, {"head": {"text": "segment contiguity", "start": 84, "end": 102}, "tail": {"text": "translation memory system", "start": 137, "end": 162}}], "evaluate for": [{"head": {"text": "retrieval", "start": 110, "end": 119}, "tail": {"text": "translation memory system", "start": 137, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We take a selection of both bag-of-words and segment order-sensitive string comparison methods , and run each over both character - and word-segmented data , in combination with a range of local segment contiguity models -LRB- in the form of N-grams -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "character - and word-segmented data", "start": 120, "end": 155}, "tail": {"text": "bag-of-words and segment order-sensitive string comparison methods", "start": 28, "end": 94}}], "conjunction": [{"head": {"text": "local segment contiguity models", "start": 189, "end": 220}, "tail": {"text": "bag-of-words and segment order-sensitive string comparison methods", "start": 28, "end": 94}}], "feature of": [{"head": {"text": "N-grams", "start": 242, "end": 249}, "tail": {"text": "local segment contiguity models", "start": 189, "end": 220}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Over two distinct datasets , we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models .", "output": {"relations": {"used for": [{"head": {"text": "character bigrams", "start": 71, "end": 88}, "tail": {"text": "indexing", "start": 42, "end": 50}}], "compare": [{"head": {"text": "character bigrams", "start": 71, "end": 88}, "tail": {"text": "word N-gram models", "start": 149, "end": 167}}], "evaluate for": [{"head": {"text": "retrieval accuracy", "start": 100, "end": 118}, "tail": {"text": "character bigrams", "start": 71, "end": 88}}, {"head": {"text": "retrieval accuracy", "start": 100, "end": 118}, "tail": {"text": "word N-gram models", "start": 149, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Further , in their optimum configuration , bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy , but much faster .", "output": {"relations": {"compare": [{"head": {"text": "bag-of-words methods", "start": 43, "end": 63}, "tail": {"text": "segment order-sensitive methods", "start": 94, "end": 125}}], "evaluate for": [{"head": {"text": "retrieval accuracy", "start": 138, "end": 156}, "tail": {"text": "bag-of-words methods", "start": 43, "end": 63}}, {"head": {"text": "retrieval accuracy", "start": 138, "end": 156}, "tail": {"text": "segment order-sensitive methods", "start": 94, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we show how two standard outputs from information extraction -LRB- IE -RRB- systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser .", "output": {"relations": {"used for": [{"head": {"text": "outputs", "start": 39, "end": 46}, "tail": {"text": "text collections", "start": 183, "end": 199}}, {"head": {"text": "text browser", "start": 215, "end": 227}, "tail": {"text": "text collections", "start": 183, "end": 199}}], "hyponym of": [{"head": {"text": "named entity annotations", "start": 100, "end": 124}, "tail": {"text": "outputs", "start": 39, "end": 46}}, {"head": {"text": "scenario templates", "start": 129, "end": 147}, "tail": {"text": "outputs", "start": 39, "end": 46}}], "conjunction": [{"head": {"text": "named entity annotations", "start": 100, "end": 124}, "tail": {"text": "scenario templates", "start": 129, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe how this information is used in a prototype system designed to support information workers ' access to a pharmaceutical news archive as part of their industry watch function .", "output": {"relations": {"used for": [{"head": {"text": "prototype system", "start": 46, "end": 62}, "tail": {"text": "pharmaceutical news archive", "start": 117, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also report results of a preliminary , qualitative user evaluation of the system , which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers .", "output": {"relations": {"evaluate for": [{"head": {"text": "qualitative user evaluation", "start": 42, "end": 69}, "tail": {"text": "system", "start": 77, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene/object from a sequence of images with unknown motions .", "output": {"relations": {"used for": [{"head": {"text": "model-based bundle adjustment algorithm", "start": 17, "end": 56}, "tail": {"text": "3D model", "start": 72, "end": 80}}, {"head": {"text": "images", "start": 118, "end": 124}, "tail": {"text": "3D model", "start": 72, "end": 80}}], "part of": [{"head": {"text": "unknown motions", "start": 130, "end": 145}, "tail": {"text": "images", "start": 118, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Instead of representing scene/object by a collection of isolated 3D features -LRB- usually points -RRB- , our algorithm uses a surface controlled by a small set of parameters .", "output": {"relations": {"used for": [{"head": {"text": "surface", "start": 127, "end": 134}, "tail": {"text": "algorithm", "start": 110, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Compared with previous model-based approaches , our approach has the following advantages .", "output": {"relations": {"compare": [{"head": {"text": "model-based approaches", "start": 23, "end": 45}, "tail": {"text": "approach", "start": 35, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , instead of using the model space as a regular-izer , we directly use it as our search space , thus resulting in a more elegant formulation with fewer unknowns and fewer equations .", "output": {"relations": {"used for": [{"head": {"text": "model space", "start": 29, "end": 40}, "tail": {"text": "regular-izer", "start": 46, "end": 58}}, {"head": {"text": "it", "start": 77, "end": 79}, "tail": {"text": "search space", "start": 87, "end": 99}}], "compare": [{"head": {"text": "regular-izer", "start": 46, "end": 58}, "tail": {"text": "search space", "start": 87, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Third , regarding face modeling , we use a very small set of face metrics -LRB- meaningful deformations -RRB- to parame-terize the face geometry , resulting in a smaller search space and a better posed system .", "output": {"relations": {"used for": [{"head": {"text": "face metrics", "start": 61, "end": 73}, "tail": {"text": "face modeling", "start": 18, "end": 31}}, {"head": {"text": "face metrics", "start": 61, "end": 73}, "tail": {"text": "face geometry", "start": 131, "end": 144}}, {"head": {"text": "face metrics", "start": 61, "end": 73}, "tail": {"text": "search space", "start": 170, "end": 182}}, {"head": {"text": "face metrics", "start": 61, "end": 73}, "tail": {"text": "posed system", "start": 196, "end": 208}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments with both synthetic and real data show that this new algorithm is faster , more accurate and more stable than existing ones .", "output": {"relations": {"evaluate for": [{"head": {"text": "synthetic and real data", "start": 22, "end": 45}, "tail": {"text": "algorithm", "start": 65, "end": 74}}, {"head": {"text": "synthetic and real data", "start": 22, "end": 45}, "tail": {"text": "ones", "start": 131, "end": 135}}], "compare": [{"head": {"text": "algorithm", "start": 65, "end": 74}, "tail": {"text": "ones", "start": 131, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an approach to the unsupervised learning of parts of speech which uses both morphological and syntactic information .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "unsupervised learning of parts of speech", "start": 39, "end": 79}}, {"head": {"text": "morphological and syntactic information", "start": 96, "end": 135}, "tail": {"text": "approach", "start": 23, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While the model is more complex than those which have been employed for unsupervised learning of POS tags in English , which use only syntactic information , the variety of languages in the world requires that we consider morphology as well .", "output": {"relations": {"compare": [{"head": {"text": "model", "start": 10, "end": 15}, "tail": {"text": "those", "start": 37, "end": 42}}], "used for": [{"head": {"text": "those", "start": 37, "end": 42}, "tail": {"text": "unsupervised learning of POS tags in English", "start": 72, "end": 116}}, {"head": {"text": "syntactic information", "start": 134, "end": 155}, "tail": {"text": "those", "start": 37, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In many languages , morphology provides better clues to a word 's category than word order .", "output": {"relations": {"compare": [{"head": {"text": "morphology", "start": 20, "end": 30}, "tail": {"text": "word order", "start": 80, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present the computational model for POS learning , and present results for applying it to Bulgarian , a Slavic language with relatively free word order and rich morphology .", "output": {"relations": {"used for": [{"head": {"text": "computational model", "start": 15, "end": 34}, "tail": {"text": "POS learning", "start": 39, "end": 51}}, {"head": {"text": "Bulgarian", "start": 93, "end": 102}, "tail": {"text": "it", "start": 87, "end": 89}}], "hyponym of": [{"head": {"text": "Bulgarian", "start": 93, "end": 102}, "tail": {"text": "Slavic language", "start": 107, "end": 122}}], "feature of": [{"head": {"text": "free word order", "start": 139, "end": 154}, "tail": {"text": "Bulgarian", "start": 93, "end": 102}}, {"head": {"text": "rich morphology", "start": 159, "end": 174}, "tail": {"text": "Bulgarian", "start": 93, "end": 102}}], "conjunction": [{"head": {"text": "free word order", "start": 139, "end": 154}, "tail": {"text": "rich morphology", "start": 159, "end": 174}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In MT , the widely used approach is to apply a Chinese word segmenter trained from manually annotated data , using a fixed lexicon .", "output": {"relations": {"used for": [{"head": {"text": "Chinese word segmenter", "start": 47, "end": 69}, "tail": {"text": "MT", "start": 3, "end": 5}}, {"head": {"text": "manually annotated data", "start": 83, "end": 106}, "tail": {"text": "Chinese word segmenter", "start": 47, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such word segmentation is not necessarily optimal for translation .", "output": {"relations": {"used for": [{"head": {"text": "word segmentation", "start": 5, "end": 22}, "tail": {"text": "translation", "start": 54, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a Bayesian semi-supervised Chinese word segmentation model which uses both monolingual and bilingual information to derive a segmentation suitable for MT .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian semi-supervised Chinese word segmentation model", "start": 13, "end": 69}, "tail": {"text": "segmentation", "start": 51, "end": 63}}, {"head": {"text": "monolingual and bilingual information", "start": 86, "end": 123}, "tail": {"text": "Bayesian semi-supervised Chinese word segmentation model", "start": 13, "end": 69}}, {"head": {"text": "segmentation", "start": 51, "end": 63}, "tail": {"text": "MT", "start": 162, "end": 164}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments show that our method improves a state-of-the-art MT system in a small and a large data environment .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 26, "end": 32}, "tail": {"text": "MT system", "start": 61, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we compare two competing approaches to part-of-speech tagging , statistical and constraint-based disambiguation , using French as our test language .", "output": {"relations": {"used for": [{"head": {"text": "approaches", "start": 39, "end": 49}, "tail": {"text": "part-of-speech tagging", "start": 53, "end": 75}}, {"head": {"text": "French", "start": 134, "end": 140}, "tail": {"text": "approaches", "start": 39, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We imposed a time limit on our experiment : the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy-to-implement statistical model .", "output": {"relations": {"compare": [{"head": {"text": "constraint system", "start": 90, "end": 107}, "tail": {"text": "statistical model", "start": 187, "end": 204}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The accuracy of the statistical method is reasonably good , comparable to taggers for English .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 4, "end": 12}, "tail": {"text": "statistical method", "start": 20, "end": 38}}, {"head": {"text": "accuracy", "start": 4, "end": 12}, "tail": {"text": "taggers", "start": 74, "end": 81}}], "compare": [{"head": {"text": "statistical method", "start": 20, "end": 38}, "tail": {"text": "taggers", "start": 74, "end": 81}}], "used for": [{"head": {"text": "taggers", "start": 74, "end": 81}, "tail": {"text": "English", "start": 86, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Structured-light methods actively generate geometric correspondence data between projectors and cameras in order to facilitate robust 3D reconstruction .", "output": {"relations": {"used for": [{"head": {"text": "Structured-light methods", "start": 0, "end": 24}, "tail": {"text": "geometric correspondence data", "start": 43, "end": 72}}, {"head": {"text": "geometric correspondence data", "start": 43, "end": 72}, "tail": {"text": "robust 3D reconstruction", "start": 127, "end": 151}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present Photogeometric Structured Light whereby a standard structured light method is extended to include photometric methods .", "output": {"relations": {"part of": [{"head": {"text": "structured light method", "start": 78, "end": 101}, "tail": {"text": "Photogeometric Structured Light", "start": 27, "end": 58}}, {"head": {"text": "photometric methods", "start": 125, "end": 144}, "tail": {"text": "Photogeometric Structured Light", "start": 27, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Photometric processing serves the double purpose of increasing the amount of recovered surface detail and of enabling the structured-light setup to be robustly self-calibrated .", "output": {"relations": {"used for": [{"head": {"text": "Photometric processing", "start": 0, "end": 22}, "tail": {"text": "recovered surface detail", "start": 77, "end": 101}}, {"head": {"text": "Photometric processing", "start": 0, "end": 22}, "tail": {"text": "structured-light setup", "start": 122, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Further , our framework uses a photogeometric optimization that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate multi-view 3D model which best complies with photometric and geometric data .", "output": {"relations": {"used for": [{"head": {"text": "photogeometric optimization", "start": 31, "end": 58}, "tail": {"text": "framework", "start": 14, "end": 23}}, {"head": {"text": "photometric and geometric data", "start": 207, "end": 237}, "tail": {"text": "multi-view 3D model", "start": 162, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , a discrimination and robustness oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution .", "output": {"relations": {"used for": [{"head": {"text": "adaptive learning procedure", "start": 57, "end": 84}, "tail": {"text": "syntactic ambiguity resolution", "start": 122, "end": 152}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Owing to the problem of insufficient training data and approximation error introduced by the language model , traditional statistical approaches , which resolve ambiguities by indirectly and implicitly using maximum likelihood method , fail to achieve high performance in real applications .", "output": {"relations": {"conjunction": [{"head": {"text": "insufficient training data", "start": 24, "end": 50}, "tail": {"text": "approximation error", "start": 55, "end": 74}}], "used for": [{"head": {"text": "statistical approaches", "start": 122, "end": 144}, "tail": {"text": "ambiguities", "start": 161, "end": 172}}, {"head": {"text": "maximum likelihood method", "start": 208, "end": 233}, "tail": {"text": "statistical approaches", "start": 122, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The accuracy rate of syntactic disambiguation is raised from 46.0 % to 60.62 % by using this novel approach .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy rate", "start": 4, "end": 17}, "tail": {"text": "syntactic disambiguation", "start": 21, "end": 45}}, {"head": {"text": "syntactic disambiguation", "start": 21, "end": 45}, "tail": {"text": "approach", "start": 99, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a new approach to statistical sentence generation in which alternative phrases are represented as packed sets of trees , or forests , and then ranked statistically to choose the best one .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 26, "end": 34}, "tail": {"text": "statistical sentence generation", "start": 38, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It also facilitates more efficient statistical ranking than a previous approach to statistical generation .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "statistical ranking", "start": 35, "end": 54}}, {"head": {"text": "approach", "start": 71, "end": 79}, "tail": {"text": "statistical generation", "start": 83, "end": 105}}], "compare": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "approach", "start": 71, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An efficient ranking algorithm is described , together with experimental results showing significant improvements over simple enumeration or a lattice-based approach .", "output": {"relations": {"compare": [{"head": {"text": "ranking algorithm", "start": 13, "end": 30}, "tail": {"text": "enumeration", "start": 126, "end": 137}}, {"head": {"text": "ranking algorithm", "start": 13, "end": 30}, "tail": {"text": "lattice-based approach", "start": 143, "end": 165}}], "conjunction": [{"head": {"text": "enumeration", "start": 126, "end": 137}, "tail": {"text": "lattice-based approach", "start": 143, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This article deals with the interpretation of conceptual operations underlying the communicative use of natural language -LRB- NL -RRB- within the Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm .", "output": {"relations": {"used for": [{"head": {"text": "natural language -LRB- NL -RRB-", "start": 104, "end": 135}, "tail": {"text": "Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm", "start": 147, "end": 206}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The operations are reduced to functions of a formal language , thus changing the level of abstraction of the operations to be performed on SI-Nets .", "output": {"relations": {"used for": [{"head": {"text": "operations", "start": 4, "end": 14}, "tail": {"text": "SI-Nets", "start": 139, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this sense , operations on SI-Nets are not merely isomorphic to single epistemological objects , but can be viewed as a simulation of processes on a different level , that pertaining to the conceptual system of NL .", "output": {"relations": {"used for": [{"head": {"text": "operations", "start": 16, "end": 26}, "tail": {"text": "SI-Nets", "start": 30, "end": 37}}, {"head": {"text": "NL", "start": 214, "end": 216}, "tail": {"text": "conceptual system", "start": 193, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For this purpose , we have designed a version of KL-ONE which represents the epistemological level , while the new experimental language , KL-Conc , represents the conceptual level .", "output": {"relations": {"compare": [{"head": {"text": "KL-ONE", "start": 49, "end": 55}, "tail": {"text": "KL-Conc", "start": 139, "end": 146}}], "feature of": [{"head": {"text": "epistemological level", "start": 77, "end": 98}, "tail": {"text": "KL-ONE", "start": 49, "end": 55}}, {"head": {"text": "conceptual level", "start": 164, "end": 180}, "tail": {"text": "KL-Conc", "start": 139, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an algorithm for calibrated camera relative pose estimation from lines .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 14, "end": 23}, "tail": {"text": "calibrated camera relative pose estimation", "start": 28, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the performance of the algorithm using synthetic and real data .", "output": {"relations": {"used for": [{"head": {"text": "synthetic and real data", "start": 51, "end": 74}, "tail": {"text": "algorithm", "start": 35, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The intended use of the algorithm is with robust hypothesize-and-test frameworks such as RANSAC .", "output": {"relations": {"conjunction": [{"head": {"text": "algorithm", "start": 24, "end": 33}, "tail": {"text": "hypothesize-and-test frameworks", "start": 49, "end": 80}}], "hyponym of": [{"head": {"text": "RANSAC", "start": 89, "end": 95}, "tail": {"text": "hypothesize-and-test frameworks", "start": 49, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our approach is suitable for urban and indoor environments where most lines are either parallel or orthogonal to each other .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "urban and indoor environments", "start": 29, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a fully automated extraction system , named IntEx , to identify gene and protein interactions in biomedical text .", "output": {"relations": {"used for": [{"head": {"text": "fully automated extraction system", "start": 29, "end": 62}, "tail": {"text": "gene and protein interactions", "start": 91, "end": 120}}, {"head": {"text": "biomedical text", "start": 124, "end": 139}, "tail": {"text": "gene and protein interactions", "start": 91, "end": 120}}], "hyponym of": [{"head": {"text": "IntEx", "start": 71, "end": 76}, "tail": {"text": "fully automated extraction system", "start": 29, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Then , tagging biological entities with the help of biomedical and linguistic ontologies .", "output": {"relations": {"used for": [{"head": {"text": "biomedical and linguistic ontologies", "start": 52, "end": 88}, "tail": {"text": "biological entities", "start": 15, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our extraction system handles complex sentences and extracts multiple and nested interactions specified in a sentence .", "output": {"relations": {"used for": [{"head": {"text": "extraction system", "start": 4, "end": 21}, "tail": {"text": "multiple and nested interactions", "start": 61, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental evaluations with two other state of the art extraction systems indicate that the IntEx system achieves better performance without the labor intensive pattern engineering requirement .", "output": {"relations": {"compare": [{"head": {"text": "IntEx system", "start": 94, "end": 106}, "tail": {"text": "extraction systems", "start": 57, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper introduces a method for computational analysis of move structures in abstracts of research articles .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 24, "end": 30}, "tail": {"text": "computational analysis of move structures", "start": 35, "end": 76}}, {"head": {"text": "abstracts of research articles", "start": 80, "end": 110}, "tail": {"text": "computational analysis of move structures", "start": 35, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method involves automatically gathering a large number of abstracts from the Web and building a language model of abstract moves .", "output": {"relations": {"used for": [{"head": {"text": "Web", "start": 81, "end": 84}, "tail": {"text": "abstracts", "start": 62, "end": 71}}, {"head": {"text": "abstract moves", "start": 118, "end": 132}, "tail": {"text": "language model", "start": 100, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also present a prototype concordancer , CARE , which exploits the move-tagged abstracts for digital learning .", "output": {"relations": {"hyponym of": [{"head": {"text": "CARE", "start": 43, "end": 47}, "tail": {"text": "prototype concordancer", "start": 18, "end": 40}}], "used for": [{"head": {"text": "CARE", "start": 43, "end": 47}, "tail": {"text": "move-tagged abstracts", "start": 69, "end": 90}}, {"head": {"text": "move-tagged abstracts", "start": 69, "end": 90}, "tail": {"text": "digital learning", "start": 95, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This system provides a promising approach to Web-based computer-assisted academic writing .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 5, "end": 11}, "tail": {"text": "approach", "start": 33, "end": 41}}, {"head": {"text": "approach", "start": 33, "end": 41}, "tail": {"text": "Web-based computer-assisted academic writing", "start": 45, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This work presents a real-time system for multiple object tracking in dynamic scenes .", "output": {"relations": {"used for": [{"head": {"text": "real-time system", "start": 21, "end": 37}, "tail": {"text": "multiple object tracking in dynamic scenes", "start": 42, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 31, "end": 37}, "tail": {"text": "long-duration and complete occlusion", "start": 66, "end": 102}}], "feature of": [{"head": {"text": "prior knowledge", "start": 113, "end": 128}, "tail": {"text": "shape", "start": 139, "end": 144}}, {"head": {"text": "prior knowledge", "start": 113, "end": 128}, "tail": {"text": "motion of objects", "start": 148, "end": 165}}], "conjunction": [{"head": {"text": "shape", "start": 139, "end": 144}, "tail": {"text": "motion of objects", "start": 148, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320x240 , as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background .", "output": {"relations": {"evaluate for": [{"head": {"text": "tracking", "start": 37, "end": 45}, "tail": {"text": "system", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a method of organizing reading materials for vocabulary learning .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "organizing reading materials", "start": 23, "end": 51}}, {"head": {"text": "organizing reading materials", "start": 23, "end": 51}, "tail": {"text": "vocabulary learning", "start": 56, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We used a specialized vocabulary for an English certification test as the target vocabulary and used English Wikipedia , a free-content encyclopedia , as the target corpus .", "output": {"relations": {"hyponym of": [{"head": {"text": "English Wikipedia", "start": 101, "end": 118}, "tail": {"text": "free-content encyclopedia", "start": 123, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A novel bootstrapping approach to Named Entity -LRB- NE -RRB- tagging using concept-based seeds and successive learners is presented .", "output": {"relations": {"used for": [{"head": {"text": "bootstrapping approach", "start": 8, "end": 30}, "tail": {"text": "Named Entity -LRB- NE -RRB- tagging", "start": 34, "end": 69}}, {"head": {"text": "concept-based seeds", "start": 76, "end": 95}, "tail": {"text": "bootstrapping approach", "start": 8, "end": 30}}, {"head": {"text": "successive learners", "start": 100, "end": 119}, "tail": {"text": "bootstrapping approach", "start": 8, "end": 30}}], "conjunction": [{"head": {"text": "concept-based seeds", "start": 76, "end": 95}, "tail": {"text": "successive learners", "start": 100, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE , e.g. he/she/man / woman for PERSON NE .", "output": {"relations": {"hyponym of": [{"head": {"text": "PERSON NE", "start": 144, "end": 153}, "tail": {"text": "NE", "start": 111, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The bootstrapping procedure is implemented as training two successive learners .", "output": {"relations": {"used for": [{"head": {"text": "successive learners", "start": 59, "end": 78}, "tail": {"text": "bootstrapping procedure", "start": 4, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , decision list is used to learn the parsing-based NE rules .", "output": {"relations": {"used for": [{"head": {"text": "decision list", "start": 8, "end": 21}, "tail": {"text": "parsing-based NE rules", "start": 43, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The resulting NE system approaches supervised NE performance for some NE types .", "output": {"relations": {"used for": [{"head": {"text": "NE system", "start": 14, "end": 23}, "tail": {"text": "supervised NE", "start": 35, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance , using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task .", "output": {"relations": {"evaluate for": [{"head": {"text": "word sense disambiguation", "start": 160, "end": 185}, "tail": {"text": "Chinese-to-English SMT model", "start": 119, "end": 147}}, {"head": {"text": "WSD evaluation methodology", "start": 215, "end": 241}, "tail": {"text": "Chinese-to-English SMT model", "start": 119, "end": 147}}, {"head": {"text": "Senseval-3 Chinese lexical sample task", "start": 264, "end": 302}, "tail": {"text": "Chinese-to-English SMT model", "start": 119, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Much effort has been put in designing and evaluating dedicated word sense disambiguation -LRB- WSD -RRB- models , in particular with the Senseval series of workshops .", "output": {"relations": {"evaluate for": [{"head": {"text": "Senseval series of workshops", "start": 137, "end": 165}, "tail": {"text": "dedicated word sense disambiguation -LRB- WSD -RRB- models", "start": 53, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "At the same time , the recent improvements in the BLEU scores of statistical machine translation -LRB- SMT -RRB- suggests that SMT models are good at predicting the right translation of the words in source language sentences .", "output": {"relations": {"evaluate for": [{"head": {"text": "BLEU scores", "start": 50, "end": 61}, "tail": {"text": "statistical machine translation -LRB- SMT -RRB-", "start": 65, "end": 112}}], "used for": [{"head": {"text": "SMT models", "start": 127, "end": 137}, "tail": {"text": "translation", "start": 85, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Surprisingly however , the WSD accuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models .", "output": {"relations": {"evaluate for": [{"head": {"text": "WSD accuracy", "start": 27, "end": 39}, "tail": {"text": "SMT models", "start": 43, "end": 53}}], "compare": [{"head": {"text": "that", "start": 97, "end": 101}, "tail": {"text": "WSD accuracy", "start": 27, "end": 39}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered .", "output": {"relations": {"evaluate for": [{"head": {"text": "WSD accuracy", "start": 46, "end": 58}, "tail": {"text": "SMT models", "start": 78, "end": 88}}], "compare": [{"head": {"text": "that", "start": 120, "end": 124}, "tail": {"text": "WSD accuracy", "start": 46, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This tends to support the view that despite recent speculative claims to the contrary , current SMT models do have limitations in comparison with dedicated WSD models , and that SMT should benefit from the better predictions made by the WSD models .", "output": {"relations": {"compare": [{"head": {"text": "SMT models", "start": 96, "end": 106}, "tail": {"text": "dedicated WSD models", "start": 146, "end": 166}}], "used for": [{"head": {"text": "WSD models", "start": 156, "end": 166}, "tail": {"text": "SMT", "start": 96, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we present a novel , customizable : IE paradigm that takes advantage of predicate-argument structures .", "output": {"relations": {"used for": [{"head": {"text": "predicate-argument structures", "start": 86, "end": 115}, "tail": {"text": "IE paradigm", "start": 50, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is based on : -LRB- 1 -RRB- an extended set of features ; and -LRB- 2 -RRB- inductive decision tree learning .", "output": {"relations": {"used for": [{"head": {"text": "features", "start": 50, "end": 58}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "inductive decision tree learning", "start": 79, "end": 111}, "tail": {"text": "It", "start": 0, "end": 2}}], "conjunction": [{"head": {"text": "features", "start": 50, "end": 58}, "tail": {"text": "inductive decision tree learning", "start": 79, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results .", "output": {"relations": {"used for": [{"head": {"text": "predicate-argument structures", "start": 55, "end": 84}, "tail": {"text": "IE", "start": 105, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we present a statistical profile of the Named Entity task , a specific information extraction task for which corpora in several languages are available .", "output": {"relations": {"used for": [{"head": {"text": "statistical profile", "start": 27, "end": 46}, "tail": {"text": "Named Entity task", "start": 54, "end": 71}}], "hyponym of": [{"head": {"text": "Named Entity task", "start": 54, "end": 71}, "tail": {"text": "information extraction task", "start": 85, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using the results of the statistical analysis , we propose an algorithm for lower bound estimation for Named Entity corpora and discuss the significance of the cross-lingual comparisons provided by the analysis .", "output": {"relations": {"used for": [{"head": {"text": "statistical analysis", "start": 25, "end": 45}, "tail": {"text": "algorithm", "start": 62, "end": 71}}, {"head": {"text": "algorithm", "start": 62, "end": 71}, "tail": {"text": "lower bound estimation", "start": 76, "end": 98}}, {"head": {"text": "lower bound estimation", "start": 76, "end": 98}, "tail": {"text": "Named Entity corpora", "start": 103, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely unsuper-vised induction of an SMT-style stochastic transduction grammar for hip hop lyrics , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .", "output": {"relations": {"hyponym of": [{"head": {"text": "lyrics in music", "start": 78, "end": 93}, "tail": {"text": "under-explored language genre of spoken language", "start": 26, "end": 74}}], "used for": [{"head": {"text": "unsuper-vised induction", "start": 112, "end": 135}, "tail": {"text": "under-explored language genre of spoken language", "start": 26, "end": 74}}, {"head": {"text": "unsuper-vised induction", "start": 112, "end": 135}, "tail": {"text": "SMT-style stochastic transduction grammar", "start": 142, "end": 183}}, {"head": {"text": "unsuper-vised induction", "start": 112, "end": 135}, "tail": {"text": "fully-automatically learned challenge-response system", "start": 216, "end": 269}}, {"head": {"text": "fully-automatically learned challenge-response system", "start": 216, "end": 269}, "tail": {"text": "rhyming lyrics", "start": 284, "end": 298}}], "feature of": [{"head": {"text": "hip hop lyrics", "start": 188, "end": 202}, "tail": {"text": "SMT-style stochastic transduction grammar", "start": 142, "end": 183}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In spite of the level of difficulty of the challenge , the model nevertheless produces fluent output as judged by human evaluators , and performs significantly better than widely used phrase-based SMT models upon the same task .", "output": {"relations": {"compare": [{"head": {"text": "model", "start": 59, "end": 64}, "tail": {"text": "phrase-based SMT models", "start": 184, "end": 207}}], "evaluate for": [{"head": {"text": "task", "start": 222, "end": 226}, "tail": {"text": "model", "start": 59, "end": 64}}, {"head": {"text": "task", "start": 222, "end": 226}, "tail": {"text": "phrase-based SMT models", "start": 184, "end": 207}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue .", "output": {"relations": {"used for": [{"head": {"text": "spoken multiparty dialogue", "start": 93, "end": 119}, "tail": {"text": "predicting segment boundaries", "start": 60, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries .", "output": {"relations": {"used for": [{"head": {"text": "approaches", "start": 15, "end": 25}, "tail": {"text": "predicting top-level topic shifts", "start": 54, "end": 87}}, {"head": {"text": "approaches", "start": 15, "end": 25}, "tail": {"text": "identifying subtopic boundaries", "start": 106, "end": 137}}], "feature of": [{"head": {"text": "predicting top-level topic shifts", "start": 54, "end": 87}, "tail": {"text": "identifying subtopic boundaries", "start": 106, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We then explore the impact on performance of using ASR output as opposed to human transcription .", "output": {"relations": {"compare": [{"head": {"text": "ASR output", "start": 51, "end": 61}, "tail": {"text": "human transcription", "start": 76, "end": 95}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks : -LRB- 1 -RRB- for predicting subtopic boundaries , the lexical cohesion-based approach alone can achieve competitive results , -LRB- 2 -RRB- for predicting top-level boundaries , the machine learning approach that combines lexical-cohesion and conversational features performs best , and -LRB- 3 -RRB- conversational cues , such as cue phrases and overlapping speech , are better indicators for the top-level prediction task .", "output": {"relations": {"part of": [{"head": {"text": "predicting subtopic boundaries", "start": 74, "end": 104}, "tail": {"text": "predicting top-level and predicting subtopic boundaries", "start": 49, "end": 104}}, {"head": {"text": "predicting top-level boundaries", "start": 275, "end": 306}, "tail": {"text": "predicting top-level and predicting subtopic boundaries", "start": 49, "end": 104}}], "used for": [{"head": {"text": "lexical cohesion-based approach", "start": 185, "end": 216}, "tail": {"text": "predicting subtopic boundaries", "start": 74, "end": 104}}, {"head": {"text": "machine learning approach", "start": 313, "end": 338}, "tail": {"text": "predicting top-level boundaries", "start": 275, "end": 306}}, {"head": {"text": "indicators", "start": 510, "end": 520}, "tail": {"text": "top-level prediction task", "start": 529, "end": 554}}], "conjunction": [{"head": {"text": "lexical-cohesion and conversational features", "start": 353, "end": 397}, "tail": {"text": "machine learning approach", "start": 313, "end": 338}}, {"head": {"text": "overlapping speech", "start": 478, "end": 496}, "tail": {"text": "cue phrases", "start": 462, "end": 473}}], "hyponym of": [{"head": {"text": "cue phrases", "start": 462, "end": 473}, "tail": {"text": "conversational cues", "start": 432, "end": 451}}, {"head": {"text": "overlapping speech", "start": 478, "end": 496}, "tail": {"text": "conversational cues", "start": 432, "end": 451}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features , but do not change the general preference of approach for the two tasks .", "output": {"relations": {"feature of": [{"head": {"text": "transcription errors", "start": 22, "end": 42}, "tail": {"text": "ASR output", "start": 57, "end": 67}}], "conjunction": [{"head": {"text": "models", "start": 94, "end": 100}, "tail": {"text": "lexical-cohesion and conversational features", "start": 114, "end": 158}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised technique", "start": 21, "end": 43}, "tail": {"text": "morphology", "start": 57, "end": 67}}, {"head": {"text": "hubs", "start": 83, "end": 87}, "tail": {"text": "unsupervised technique", "start": 21, "end": 43}}], "part of": [{"head": {"text": "hubs", "start": 83, "end": 87}, "tail": {"text": "automaton", "start": 94, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For our purposes , a hub is a node in a graph with in-degree greater than one and out-degree greater than one .", "output": {"relations": {"hyponym of": [{"head": {"text": "hub", "start": 21, "end": 24}, "tail": {"text": "node", "start": 30, "end": 34}}], "part of": [{"head": {"text": "node", "start": 30, "end": 34}, "tail": {"text": "graph", "start": 40, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We create a word-trie , transform it into a minimal DFA , then identify hubs .", "output": {"relations": {"used for": [{"head": {"text": "word-trie", "start": 12, "end": 21}, "tail": {"text": "hubs", "start": 72, "end": 76}}, {"head": {"text": "minimal DFA", "start": 44, "end": 55}, "tail": {"text": "hubs", "start": 72, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In Bayesian machine learning , conjugate priors are popular , mostly due to mathematical convenience .", "output": {"relations": {"part of": [{"head": {"text": "conjugate priors", "start": 31, "end": 47}, "tail": {"text": "Bayesian machine learning", "start": 3, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive .", "output": {"relations": {"feature of": [{"head": {"text": "Bregman divergence", "start": 63, "end": 81}, "tail": {"text": "conjugate prior", "start": 32, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We use this geometric understanding of conjugate priors to derive the hyperparameters and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning .", "output": {"relations": {"used for": [{"head": {"text": "geometric understanding of conjugate priors", "start": 12, "end": 55}, "tail": {"text": "hyperparameters", "start": 70, "end": 85}}, {"head": {"text": "prior", "start": 49, "end": 54}, "tail": {"text": "generative and discriminative components", "start": 133, "end": 173}}, {"head": {"text": "hybrid model", "start": 179, "end": 191}, "tail": {"text": "semi-supervised learning", "start": 196, "end": 220}}], "part of": [{"head": {"text": "generative and discriminative components", "start": 133, "end": 173}, "tail": {"text": "hybrid model", "start": 179, "end": 191}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper defines a generative probabilistic model of parse trees , which we call PCFG-LA .", "output": {"relations": {"hyponym of": [{"head": {"text": "PCFG-LA", "start": 83, "end": 90}, "tail": {"text": "generative probabilistic model of parse trees", "start": 21, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables .", "output": {"relations": {"used for": [{"head": {"text": "PCFG", "start": 30, "end": 34}, "tail": {"text": "model", "start": 5, "end": 10}}, {"head": {"text": "latent variables", "start": 84, "end": 100}, "tail": {"text": "non-terminal symbols", "start": 44, "end": 64}}], "part of": [{"head": {"text": "non-terminal symbols", "start": 44, "end": 64}, "tail": {"text": "PCFG", "start": 30, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finegrained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm .", "output": {"relations": {"used for": [{"head": {"text": "parsed corpus", "start": 55, "end": 68}, "tail": {"text": "Finegrained CFG rules", "start": 0, "end": 21}}, {"head": {"text": "EM-algorithm", "start": 106, "end": 118}, "tail": {"text": "PCFG-LA model", "start": 83, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Because exact parsing with a PCFG-LA is NP-hard , several approximations are described and empirically compared .", "output": {"relations": {"used for": [{"head": {"text": "PCFG-LA", "start": 29, "end": 36}, "tail": {"text": "exact parsing", "start": 8, "end": 21}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In experiments using the Penn WSJ corpus , our automatically trained model gave a performance of 86.6 % -LRB- F1 , sentences < 40 words -RRB- , which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection .", "output": {"relations": {"evaluate for": [{"head": {"text": "Penn WSJ corpus", "start": 25, "end": 40}, "tail": {"text": "model", "start": 69, "end": 74}}, {"head": {"text": "Penn WSJ corpus", "start": 25, "end": 40}, "tail": {"text": "unlexicalized PCFG parser", "start": 178, "end": 203}}, {"head": {"text": "F1", "start": 110, "end": 112}, "tail": {"text": "model", "start": 69, "end": 74}}, {"head": {"text": "F1", "start": 110, "end": 112}, "tail": {"text": "unlexicalized PCFG parser", "start": 178, "end": 203}}], "compare": [{"head": {"text": "model", "start": 69, "end": 74}, "tail": {"text": "unlexicalized PCFG parser", "start": 178, "end": 203}}], "used for": [{"head": {"text": "manual feature selection", "start": 228, "end": 252}, "tail": {"text": "unlexicalized PCFG parser", "start": 178, "end": 203}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , we present a new paradigm for speaker-independent -LRB- SI -RRB- training of hidden Markov models -LRB- HMM -RRB- , which uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from many speakers .", "output": {"relations": {"used for": [{"head": {"text": "speech", "start": 153, "end": 159}, "tail": {"text": "speaker-independent -LRB- SI -RRB- training of hidden Markov models -LRB- HMM -RRB-", "start": 38, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In addition , combination of the training speakers is done by averaging the statistics of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training .", "output": {"relations": {"compare": [{"head": {"text": "averaging the statistics of independently trained models", "start": 62, "end": 118}, "tail": {"text": "pooling of all the speech data", "start": 141, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With only 12 training speakers for SI recognition , we achieved a 7.5 % word error rate on a standard grammar and test set from the DARPA Resource Management corpus .", "output": {"relations": {"evaluate for": [{"head": {"text": "word error rate", "start": 72, "end": 87}, "tail": {"text": "SI recognition", "start": 35, "end": 49}}, {"head": {"text": "DARPA Resource Management corpus", "start": 132, "end": 164}, "tail": {"text": "SI recognition", "start": 35, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , we show a significant improvement for speaker adaptation -LRB- SA -RRB- using the new SI corpus and a small amount of speech from the new -LRB- target -RRB- speaker .", "output": {"relations": {"evaluate for": [{"head": {"text": "SI corpus", "start": 95, "end": 104}, "tail": {"text": "speaker adaptation -LRB- SA -RRB-", "start": 47, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using only 40 utterances from the target speaker for adaptation , the error rate dropped to 4.1 % -- a 45 % reduction in error compared to the SI result .", "output": {"relations": {"evaluate for": [{"head": {"text": "error rate", "start": 70, "end": 80}, "tail": {"text": "adaptation", "start": 53, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Dictionary construction , one of the most difficult tasks in developing a machine translation system , is expensive .", "output": {"relations": {"part of": [{"head": {"text": "Dictionary construction", "start": 0, "end": 23}, "tail": {"text": "machine translation system", "start": 74, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To avoid this problem , we investigate how we build a dictionary using existing linguistic resources .", "output": {"relations": {"used for": [{"head": {"text": "linguistic resources", "start": 80, "end": 100}, "tail": {"text": "dictionary", "start": 54, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our algorithm can be applied to any language pairs , but for the present we focus on building a Korean-to-Japanese dictionary using English as a pivot .", "output": {"relations": {"used for": [{"head": {"text": "English", "start": 132, "end": 139}, "tail": {"text": "Korean-to-Japanese dictionary", "start": 96, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We attempt three ways of automatic construction to corroborate the effect of the directionality of dictionaries .", "output": {"relations": {"evaluate for": [{"head": {"text": "automatic construction", "start": 25, "end": 47}, "tail": {"text": "directionality of dictionaries", "start": 81, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , we introduce `` one-time look up '' method using a Korean-to-English and a Japanese-to-English dictionary .", "output": {"relations": {"used for": [{"head": {"text": "Korean-to-English and a Japanese-to-English dictionary", "start": 59, "end": 113}, "tail": {"text": "`` one-time look up '' method", "start": 21, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , we show a method using `` overlapping constraint '' with a Korean-to-English dictionary and an English-to-Japanese dictionary .", "output": {"relations": {"used for": [{"head": {"text": "`` overlapping constraint ''", "start": 32, "end": 60}, "tail": {"text": "method", "start": 19, "end": 25}}, {"head": {"text": "Korean-to-English dictionary", "start": 68, "end": 96}, "tail": {"text": "method", "start": 19, "end": 25}}, {"head": {"text": "English-to-Japanese dictionary", "start": 104, "end": 134}, "tail": {"text": "method", "start": 19, "end": 25}}], "conjunction": [{"head": {"text": "Korean-to-English dictionary", "start": 68, "end": 96}, "tail": {"text": "English-to-Japanese dictionary", "start": 104, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Third , we consider another alternative method rarely used for building a dictionary : an English-to-Korean dictionary and English-to-Japanese dictionary .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 40, "end": 46}, "tail": {"text": "dictionary", "start": 74, "end": 84}}], "hyponym of": [{"head": {"text": "English-to-Korean dictionary", "start": 90, "end": 118}, "tail": {"text": "dictionary", "start": 74, "end": 84}}, {"head": {"text": "English-to-Japanese dictionary", "start": 123, "end": 153}, "tail": {"text": "dictionary", "start": 74, "end": 84}}], "conjunction": [{"head": {"text": "English-to-Korean dictionary", "start": 90, "end": 118}, "tail": {"text": "English-to-Japanese dictionary", "start": 123, "end": 153}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An empirical comparison of CFG filtering techniques for LTAG and HPSG is presented .", "output": {"relations": {"used for": [{"head": {"text": "CFG filtering techniques", "start": 27, "end": 51}, "tail": {"text": "LTAG", "start": 56, "end": 60}}, {"head": {"text": "CFG filtering techniques", "start": 27, "end": 51}, "tail": {"text": "HPSG", "start": 65, "end": 69}}], "compare": [{"head": {"text": "LTAG", "start": 56, "end": 60}, "tail": {"text": "HPSG", "start": 65, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate that an approximation of HPSG produces a more effective CFG filter than that of LTAG .", "output": {"relations": {"used for": [{"head": {"text": "approximation of HPSG", "start": 23, "end": 44}, "tail": {"text": "CFG filter", "start": 71, "end": 81}}, {"head": {"text": "that", "start": 15, "end": 19}, "tail": {"text": "LTAG", "start": 95, "end": 99}}], "compare": [{"head": {"text": "CFG filter", "start": 71, "end": 81}, "tail": {"text": "that", "start": 15, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Syntax-based statistical machine translation -LRB- MT -RRB- aims at applying statistical models to structured data .", "output": {"relations": {"used for": [{"head": {"text": "statistical models", "start": 77, "end": 95}, "tail": {"text": "Syntax-based statistical machine translation -LRB- MT -RRB-", "start": 0, "end": 59}}, {"head": {"text": "structured data", "start": 99, "end": 114}, "tail": {"text": "statistical models", "start": 77, "end": 95}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar .", "output": {"relations": {"used for": [{"head": {"text": "probabilistic synchronous dependency insertion grammar", "start": 92, "end": 146}, "tail": {"text": "syntax-based statistical machine translation system", "start": 29, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees .", "output": {"relations": {"hyponym of": [{"head": {"text": "Synchronous dependency insertion grammars", "start": 0, "end": 41}, "tail": {"text": "synchronous grammars", "start": 59, "end": 79}}], "feature of": [{"head": {"text": "dependency trees", "start": 91, "end": 107}, "tail": {"text": "Synchronous dependency insertion grammars", "start": 0, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We first introduce our approach to inducing such a grammar from parallel corpora .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "grammar", "start": 51, "end": 58}}, {"head": {"text": "parallel corpora", "start": 64, "end": 80}, "tail": {"text": "grammar", "start": 51, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , we describe the graphical model for the machine translation task , which can also be viewed as a stochastic tree-to-tree transducer .", "output": {"relations": {"used for": [{"head": {"text": "graphical model", "start": 25, "end": 40}, "tail": {"text": "machine translation task", "start": 49, "end": 73}}, {"head": {"text": "stochastic tree-to-tree transducer", "start": 106, "end": 140}, "tail": {"text": "graphical model", "start": 25, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce a polynomial time decoding algorithm for the model .", "output": {"relations": {"used for": [{"head": {"text": "polynomial time decoding algorithm", "start": 15, "end": 49}, "tail": {"text": "model", "start": 58, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software .", "output": {"relations": {"used for": [{"head": {"text": "NIST and Bleu automatic MT evaluation software", "start": 51, "end": 97}, "tail": {"text": "MT system", "start": 31, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 26, "end": 32}, "tail": {"text": "baseline system", "start": 49, "end": 64}}], "used for": [{"head": {"text": "IBM models", "start": 78, "end": 88}, "tail": {"text": "baseline system", "start": 49, "end": 64}}], "evaluate for": [{"head": {"text": "translation speed and quality", "start": 97, "end": 126}, "tail": {"text": "system", "start": 26, "end": 32}}, {"head": {"text": "translation speed and quality", "start": 97, "end": 126}, "tail": {"text": "baseline system", "start": 49, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a framework to derive the distance between concepts from distributional measures of word co-occurrences .", "output": {"relations": {"used for": [{"head": {"text": "distributional measures of word co-occurrences", "start": 68, "end": 114}, "tail": {"text": "framework", "start": 13, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the tasks of -LRB- 1 -RRB- ranking word pairs in order of semantic distance , and -LRB- 2 -RRB- correcting real-word spelling errors .", "output": {"relations": {"used for": [{"head": {"text": "concept-distance measures", "start": 32, "end": 57}, "tail": {"text": "tasks", "start": 126, "end": 131}}, {"head": {"text": "distributional word-distance measures", "start": 81, "end": 118}, "tail": {"text": "tasks", "start": 126, "end": 131}}], "compare": [{"head": {"text": "distributional word-distance measures", "start": 81, "end": 118}, "tail": {"text": "concept-distance measures", "start": 32, "end": 57}}], "hyponym of": [{"head": {"text": "ranking word pairs in order of semantic distance", "start": 149, "end": 197}, "tail": {"text": "tasks", "start": 126, "end": 131}}, {"head": {"text": "correcting real-word spelling errors", "start": 218, "end": 254}, "tail": {"text": "tasks", "start": 126, "end": 131}}], "conjunction": [{"head": {"text": "correcting real-word spelling errors", "start": 218, "end": 254}, "tail": {"text": "ranking word pairs in order of semantic distance", "start": 149, "end": 197}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the latter task , of all the WordNet-based measures , only that proposed by Jiang and Conrath outperforms the best distributional concept-distance measures .", "output": {"relations": {"evaluate for": [{"head": {"text": "task", "start": 14, "end": 18}, "tail": {"text": "WordNet-based measures", "start": 32, "end": 54}}, {"head": {"text": "task", "start": 14, "end": 18}, "tail": {"text": "distributional concept-distance measures", "start": 118, "end": 158}}], "compare": [{"head": {"text": "distributional concept-distance measures", "start": 118, "end": 158}, "tail": {"text": "WordNet-based measures", "start": 32, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One of the main results of this work is the definition of a relation between broad semantic classes and LCS meaning components .", "output": {"relations": {"conjunction": [{"head": {"text": "broad semantic classes", "start": 77, "end": 99}, "tail": {"text": "LCS meaning components", "start": 104, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our acquisition program - LEXICALL - takes , as input , the result of previous work on verb classification and thematic grid tagging , and outputs LCS representations for different languages .", "output": {"relations": {"used for": [{"head": {"text": "acquisition program - LEXICALL -", "start": 4, "end": 36}, "tail": {"text": "LCS representations", "start": 147, "end": 166}}, {"head": {"text": "verb classification", "start": 87, "end": 106}, "tail": {"text": "acquisition program - LEXICALL -", "start": 4, "end": 36}}, {"head": {"text": "thematic grid tagging", "start": 111, "end": 132}, "tail": {"text": "acquisition program - LEXICALL -", "start": 4, "end": 36}}], "conjunction": [{"head": {"text": "verb classification", "start": 87, "end": 106}, "tail": {"text": "thematic grid tagging", "start": 111, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These representations have been ported into English , Arabic and Spanish lexicons , each containing approximately 9000 verbs .", "output": {"relations": {"used for": [{"head": {"text": "representations", "start": 6, "end": 21}, "tail": {"text": "English , Arabic and Spanish lexicons", "start": 44, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We are currently using these lexicons in an operational foreign language tutoring and machine translation .", "output": {"relations": {"used for": [{"head": {"text": "lexicons", "start": 29, "end": 37}, "tail": {"text": "operational foreign language tutoring", "start": 44, "end": 81}}, {"head": {"text": "lexicons", "start": 29, "end": 37}, "tail": {"text": "machine translation", "start": 86, "end": 105}}], "conjunction": [{"head": {"text": "operational foreign language tutoring", "start": 44, "end": 81}, "tail": {"text": "machine translation", "start": 86, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The theoretical study of the range concatenation grammar -LSB- RCG -RSB- formalism has revealed many attractive properties which may be used in NLP .", "output": {"relations": {"used for": [{"head": {"text": "range concatenation grammar -LSB- RCG -RSB- formalism", "start": 29, "end": 82}, "tail": {"text": "NLP", "start": 144, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , range concatenation languages -LSB- RCL -RSB- can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent RCGs without increasing their worst-case parsing time complexity .", "output": {"relations": {"feature of": [{"head": {"text": "polynomial time", "start": 79, "end": 94}, "tail": {"text": "range concatenation languages -LSB- RCL -RSB-", "start": 16, "end": 61}}], "evaluate for": [{"head": {"text": "worst-case parsing time complexity", "start": 201, "end": 235}, "tail": {"text": "grammatical formalisms", "start": 114, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For example , after translation into an equivalent RCG , any tree adjoining grammar can be parsed in O -LRB- n6 -RRB- time .", "output": {"relations": {"feature of": [{"head": {"text": "O -LRB- n6 -RRB- time", "start": 101, "end": 122}, "tail": {"text": "tree adjoining grammar", "start": 61, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we study a parsing technique whose purpose is to improve the practical efficiency of RCL parsers .", "output": {"relations": {"used for": [{"head": {"text": "parsing technique", "start": 27, "end": 44}, "tail": {"text": "RCL parsers", "start": 101, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L .", "output": {"relations": {"used for": [{"head": {"text": "main parser", "start": 45, "end": 56}, "tail": {"text": "language L", "start": 63, "end": 73}}, {"head": {"text": "RCL parser", "start": 156, "end": 166}, "tail": {"text": "shared derivation forest", "start": 113, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results of a practical evaluation of this method on a wide coverage English grammar are given .", "output": {"relations": {"evaluate for": [{"head": {"text": "wide coverage English grammar", "start": 58, "end": 87}, "tail": {"text": "method", "start": 46, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we introduce Ant-Q , a family of algorithms which present many similarities with Q-learning -LRB- Watkins , 1989 -RRB- , and which we apply to the solution of symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "Ant-Q", "start": 27, "end": 32}, "tail": {"text": "symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB-", "start": 173, "end": 258}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Ant-Q algorithms were inspired by work on the ant system -LRB- AS -RRB- , a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in -LRB- Dorigo , 1992 ; Dorigo , Maniezzo and Colorni , 1996 -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "ant system -LRB- AS -RRB-", "start": 46, "end": 71}, "tail": {"text": "Ant-Q algorithms", "start": 0, "end": 16}}, {"head": {"text": "distributed algorithm", "start": 76, "end": 97}, "tail": {"text": "combinatorial optimization", "start": 102, "end": 128}}], "hyponym of": [{"head": {"text": "ant system -LRB- AS -RRB-", "start": 46, "end": 71}, "tail": {"text": "distributed algorithm", "start": 76, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that AS is a particular instance of the Ant-Q family , and that there are instances of this family which perform better than AS .", "output": {"relations": {"hyponym of": [{"head": {"text": "AS", "start": 13, "end": 15}, "tail": {"text": "Ant-Q family", "start": 48, "end": 60}}], "part of": [{"head": {"text": "instances", "start": 82, "end": 91}, "tail": {"text": "family", "start": 54, "end": 60}}], "compare": [{"head": {"text": "instances", "start": 82, "end": 91}, "tail": {"text": "AS", "start": 13, "end": 15}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP 's are competitive with those obtained by other heuristic approaches based on neural networks or local search .", "output": {"relations": {"used for": [{"head": {"text": "Ant-Q", "start": 49, "end": 54}, "tail": {"text": "symmetric TSP", "start": 105, "end": 118}}, {"head": {"text": "neural networks", "start": 197, "end": 212}, "tail": {"text": "heuristic approaches", "start": 167, "end": 187}}, {"head": {"text": "local search", "start": 216, "end": 228}, "tail": {"text": "heuristic approaches", "start": 167, "end": 187}}], "compare": [{"head": {"text": "Ant-Q", "start": 49, "end": 54}, "tail": {"text": "heuristic approaches", "start": 167, "end": 187}}], "conjunction": [{"head": {"text": "neural networks", "start": 197, "end": 212}, "tail": {"text": "local search", "start": 216, "end": 228}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we apply Ant-Q to some difficult asymmetric TSP 's obtaining very good results : Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms .", "output": {"relations": {"used for": [{"head": {"text": "Ant-Q", "start": 19, "end": 24}, "tail": {"text": "asymmetric TSP", "start": 43, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification .", "output": {"relations": {"used for": [{"head": {"text": "geometric framework", "start": 29, "end": 48}, "tail": {"text": "linear or nonlinear discriminant subspace learning and classification", "start": 53, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our framework , the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a submanifold embedded in an ambient semi-Riemannian space .", "output": {"relations": {"used for": [{"head": {"text": "semi-Riemannian manifold", "start": 69, "end": 93}, "tail": {"text": "structures of classes", "start": 23, "end": 44}}], "part of": [{"head": {"text": "submanifold", "start": 119, "end": 130}, "tail": {"text": "ambient semi-Riemannian space", "start": 146, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The class structures of original samples can be characterized and deformed by local metrics of the semi-Riemannian space .", "output": {"relations": {"used for": [{"head": {"text": "local metrics of the semi-Riemannian space", "start": 78, "end": 120}, "tail": {"text": "class structures", "start": 4, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Semi-Riemannian metrics are uniquely determined by the smoothing of discrete functions and the nullity of the semi-Riemannian space .", "output": {"relations": {"used for": [{"head": {"text": "smoothing of discrete functions", "start": 55, "end": 86}, "tail": {"text": "Semi-Riemannian metrics", "start": 0, "end": 23}}, {"head": {"text": "nullity of the semi-Riemannian space", "start": 95, "end": 131}, "tail": {"text": "Semi-Riemannian metrics", "start": 0, "end": 23}}], "conjunction": [{"head": {"text": "smoothing of discrete functions", "start": 55, "end": 86}, "tail": {"text": "nullity of the semi-Riemannian space", "start": 95, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on the geometrization of class structures , optimizing class structures in the feature space is equivalent to maximizing the quadratic quantities of metric tensors in the semi-Riemannian space .", "output": {"relations": {"feature of": [{"head": {"text": "feature space", "start": 85, "end": 98}, "tail": {"text": "class structures", "start": 31, "end": 47}}, {"head": {"text": "semi-Riemannian space", "start": 177, "end": 198}, "tail": {"text": "quadratic quantities of metric tensors", "start": 131, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on the proposed framework , a novel algorithm , dubbed as Semi-Riemannian Discriminant Analysis -LRB- SRDA -RRB- , is presented for subspace-based classification .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 22, "end": 31}, "tail": {"text": "algorithm", "start": 42, "end": 51}}, {"head": {"text": "algorithm", "start": 42, "end": 51}, "tail": {"text": "subspace-based classification", "start": 138, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The performance of SRDA is tested on face recognition -LRB- singular case -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing algorithms .", "output": {"relations": {"compare": [{"head": {"text": "SRDA", "start": 19, "end": 23}, "tail": {"text": "algorithms", "start": 172, "end": 182}}], "evaluate for": [{"head": {"text": "face recognition -LRB- singular case", "start": 37, "end": 73}, "tail": {"text": "SRDA", "start": 19, "end": 23}}, {"head": {"text": "face recognition -LRB- singular case", "start": 37, "end": 73}, "tail": {"text": "algorithms", "start": 172, "end": 182}}, {"head": {"text": "handwritten capital letter classification -LRB- nonsingular case -RRB-", "start": 84, "end": 154}, "tail": {"text": "SRDA", "start": 19, "end": 23}}, {"head": {"text": "handwritten capital letter classification -LRB- nonsingular case -RRB-", "start": 84, "end": 154}, "tail": {"text": "algorithms", "start": 172, "end": 182}}], "conjunction": [{"head": {"text": "face recognition -LRB- singular case", "start": 37, "end": 73}, "tail": {"text": "handwritten capital letter classification -LRB- nonsingular case -RRB-", "start": 84, "end": 154}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results show that SRDA works well on recognition and classification , implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning .", "output": {"relations": {"used for": [{"head": {"text": "SRDA", "start": 35, "end": 39}, "tail": {"text": "recognition", "start": 54, "end": 65}}, {"head": {"text": "SRDA", "start": 35, "end": 39}, "tail": {"text": "classification", "start": 70, "end": 84}}, {"head": {"text": "semi-Riemannian geometry", "start": 101, "end": 125}, "tail": {"text": "pattern recognition", "start": 154, "end": 173}}, {"head": {"text": "semi-Riemannian geometry", "start": 101, "end": 125}, "tail": {"text": "machine learning", "start": 178, "end": 194}}], "conjunction": [{"head": {"text": "recognition", "start": 54, "end": 65}, "tail": {"text": "classification", "start": 70, "end": 84}}, {"head": {"text": "pattern recognition", "start": 154, "end": 173}, "tail": {"text": "machine learning", "start": 178, "end": 194}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A deterministic parser is under development which represents a departure from traditional deterministic parsers in that it combines both symbolic and connectionist components .", "output": {"relations": {"compare": [{"head": {"text": "deterministic parser", "start": 2, "end": 22}, "tail": {"text": "deterministic parsers", "start": 90, "end": 111}}], "part of": [{"head": {"text": "symbolic and connectionist components", "start": 137, "end": 174}, "tail": {"text": "it", "start": 82, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The connectionist component is trained either from patterns derived from the rules of a deterministic grammar .", "output": {"relations": {"used for": [{"head": {"text": "patterns", "start": 51, "end": 59}, "tail": {"text": "connectionist component", "start": 4, "end": 27}}, {"head": {"text": "rules of a deterministic grammar", "start": 77, "end": 109}, "tail": {"text": "patterns", "start": 51, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The development and evolution of such a hybrid architecture has lead to a parser which is superior to any known deterministic parser .", "output": {"relations": {"used for": [{"head": {"text": "hybrid architecture", "start": 40, "end": 59}, "tail": {"text": "parser", "start": 74, "end": 80}}], "compare": [{"head": {"text": "parser", "start": 74, "end": 80}, "tail": {"text": "deterministic parser", "start": 112, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments are described and powerful training techniques are demonstrated that permit decision-making by the connectionist component in the parsing process .", "output": {"relations": {"used for": [{"head": {"text": "training techniques", "start": 39, "end": 58}, "tail": {"text": "decision-making", "start": 88, "end": 103}}, {"head": {"text": "connectionist component", "start": 111, "end": 134}, "tail": {"text": "decision-making", "start": 88, "end": 103}}], "part of": [{"head": {"text": "connectionist component", "start": 111, "end": 134}, "tail": {"text": "parsing process", "start": 142, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Data are presented which show how a connectionist -LRB- neural -RRB- network trained with linguistic rules can parse both expected -LRB- grammatical -RRB- sentences as well as some novel -LRB- ungrammatical or lexically ambiguous -RRB- sentences .", "output": {"relations": {"used for": [{"head": {"text": "connectionist -LRB- neural -RRB- network", "start": 36, "end": 76}, "tail": {"text": "expected -LRB- grammatical -RRB- sentences", "start": 122, "end": 164}}, {"head": {"text": "connectionist -LRB- neural -RRB- network", "start": 36, "end": 76}, "tail": {"text": "-LRB- ungrammatical or lexically ambiguous -RRB- sentences", "start": 187, "end": 245}}, {"head": {"text": "linguistic rules", "start": 90, "end": 106}, "tail": {"text": "connectionist -LRB- neural -RRB- network", "start": 36, "end": 76}}], "conjunction": [{"head": {"text": "expected -LRB- grammatical -RRB- sentences", "start": 122, "end": 164}, "tail": {"text": "-LRB- ungrammatical or lexically ambiguous -RRB- sentences", "start": 187, "end": 245}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Robust natural language interpretation requires strong semantic domain models , fail-soft recovery heuristics , and very flexible control structures .", "output": {"relations": {"used for": [{"head": {"text": "semantic domain models", "start": 55, "end": 77}, "tail": {"text": "natural language interpretation", "start": 7, "end": 38}}, {"head": {"text": "fail-soft recovery heuristics", "start": 80, "end": 109}, "tail": {"text": "natural language interpretation", "start": 7, "end": 38}}, {"head": {"text": "control structures", "start": 130, "end": 148}, "tail": {"text": "natural language interpretation", "start": 7, "end": 38}}], "conjunction": [{"head": {"text": "semantic domain models", "start": 55, "end": 77}, "tail": {"text": "fail-soft recovery heuristics", "start": 80, "end": 109}}, {"head": {"text": "fail-soft recovery heuristics", "start": 80, "end": 109}, "tail": {"text": "control structures", "start": 130, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although single-strategy parsers have met with a measure of success , a multi-strategy approach is shown to provide a much higher degree of flexibility , redundancy , and ability to bring task-specific domain knowledge -LRB- in addition to general linguistic knowledge -RRB- to bear on both grammatical and ungrammatical input .", "output": {"relations": {"compare": [{"head": {"text": "single-strategy parsers", "start": 9, "end": 32}, "tail": {"text": "multi-strategy approach", "start": 72, "end": 95}}], "conjunction": [{"head": {"text": "task-specific domain knowledge", "start": 188, "end": 218}, "tail": {"text": "general linguistic knowledge", "start": 240, "end": 268}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A parsing algorithm is presented that integrates several different parsing strategies , with case-frame instantiation dominating .", "output": {"relations": {"part of": [{"head": {"text": "parsing strategies", "start": 67, "end": 85}, "tail": {"text": "parsing algorithm", "start": 2, "end": 19}}], "hyponym of": [{"head": {"text": "case-frame instantiation", "start": 93, "end": 117}, "tail": {"text": "parsing strategies", "start": 67, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , fragmentary input , and ungrammatical structures , as well as less exotic , grammatically correct input .", "output": {"relations": {"used for": [{"head": {"text": "parsing strategies", "start": 14, "end": 32}, "tail": {"text": "conjunctions", "start": 143, "end": 155}}, {"head": {"text": "parsing strategies", "start": 14, "end": 32}, "tail": {"text": "fragmentary input", "start": 158, "end": 175}}, {"head": {"text": "parsing strategies", "start": 14, "end": 32}, "tail": {"text": "ungrammatical structures", "start": 182, "end": 206}}, {"head": {"text": "parsing strategies", "start": 14, "end": 32}, "tail": {"text": "exotic , grammatically correct input", "start": 225, "end": 261}}], "conjunction": [{"head": {"text": "conjunctions", "start": 143, "end": 155}, "tail": {"text": "fragmentary input", "start": 158, "end": 175}}, {"head": {"text": "fragmentary input", "start": 158, "end": 175}, "tail": {"text": "ungrammatical structures", "start": 182, "end": 206}}, {"head": {"text": "ungrammatical structures", "start": 182, "end": 206}, "tail": {"text": "exotic , grammatically correct input", "start": 225, "end": 261}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Several specific heuristics for handling ungrammatical input are presented within this multi-strategy framework .", "output": {"relations": {"used for": [{"head": {"text": "specific heuristics", "start": 8, "end": 27}, "tail": {"text": "ungrammatical input", "start": 41, "end": 60}}], "part of": [{"head": {"text": "specific heuristics", "start": 8, "end": 27}, "tail": {"text": "multi-strategy framework", "start": 87, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recently , Stacked Auto-Encoders -LRB- SAE -RRB- have been successfully used for learning imbalanced datasets .", "output": {"relations": {"used for": [{"head": {"text": "Stacked Auto-Encoders -LRB- SAE -RRB-", "start": 11, "end": 48}, "tail": {"text": "learning imbalanced datasets", "start": 81, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , for the first time , we propose to use a Neural Network classifier furnished by an SAE structure for detecting the errors made by a strong Automatic Speech Recognition -LRB- ASR -RRB- system .", "output": {"relations": {"used for": [{"head": {"text": "Neural Network classifier", "start": 57, "end": 82}, "tail": {"text": "Automatic Speech Recognition -LRB- ASR -RRB- system", "start": 155, "end": 206}}, {"head": {"text": "SAE structure", "start": 99, "end": 112}, "tail": {"text": "Neural Network classifier", "start": 57, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Error detection on an automatic transcription provided by a '' strong '' ASR system , i.e. exhibiting a small word error rate , is difficult due to the limited number of '' positive '' examples -LRB- i.e. words erroneously recognized -RRB- available for training a binary classi-fier .", "output": {"relations": {"used for": [{"head": {"text": "Error detection", "start": 0, "end": 15}, "tail": {"text": "automatic transcription", "start": 22, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we investigate and compare different types of classifiers for automatically detecting ASR errors , including the one based on a stacked auto-encoder architecture .", "output": {"relations": {"used for": [{"head": {"text": "classifiers", "start": 60, "end": 71}, "tail": {"text": "automatically detecting ASR errors", "start": 76, "end": 110}}, {"head": {"text": "stacked auto-encoder architecture", "start": 142, "end": 175}, "tail": {"text": "one", "start": 127, "end": 130}}], "hyponym of": [{"head": {"text": "one", "start": 127, "end": 130}, "tail": {"text": "classifiers", "start": 60, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show the effectiveness of the latter by measuring and comparing performance on the automatic transcriptions of an English corpus collected from TED talks .", "output": {"relations": {"feature of": [{"head": {"text": "English corpus", "start": 117, "end": 131}, "tail": {"text": "automatic transcriptions", "start": 86, "end": 110}}], "used for": [{"head": {"text": "TED talks", "start": 147, "end": 156}, "tail": {"text": "English corpus", "start": 117, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Performance of each investigated classifier is evaluated both via receiving operating curve and via a measure , called mean absolute error , related to the quality in predicting the corresponding word error rate .", "output": {"relations": {"evaluate for": [{"head": {"text": "receiving operating curve", "start": 66, "end": 91}, "tail": {"text": "classifier", "start": 33, "end": 43}}, {"head": {"text": "measure", "start": 102, "end": 109}, "tail": {"text": "classifier", "start": 33, "end": 43}}], "conjunction": [{"head": {"text": "receiving operating curve", "start": 66, "end": 91}, "tail": {"text": "measure", "start": 102, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results demonstrates that the classifier based on SAE detects the ASR errors better than the other classification methods .", "output": {"relations": {"used for": [{"head": {"text": "classifier", "start": 34, "end": 44}, "tail": {"text": "ASR errors", "start": 70, "end": 80}}, {"head": {"text": "SAE", "start": 54, "end": 57}, "tail": {"text": "classifier", "start": 34, "end": 44}}, {"head": {"text": "classification methods", "start": 103, "end": 125}, "tail": {"text": "ASR errors", "start": 70, "end": 80}}], "compare": [{"head": {"text": "classifier", "start": 34, "end": 44}, "tail": {"text": "classification methods", "start": 103, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Within the EU Network of Excellence PASCAL , a challenge was organized to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language , morphemes .", "output": {"relations": {"used for": [{"head": {"text": "statistical machine learning algorithm", "start": 83, "end": 121}, "tail": {"text": "smallest meaning-bearing units of language", "start": 151, "end": 193}}], "hyponym of": [{"head": {"text": "morphemes", "start": 196, "end": 205}, "tail": {"text": "smallest meaning-bearing units of language", "start": 151, "end": 193}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Ideally , these are basic vocabulary units suitable for different tasks , such as speech and text understanding , machine translation , information retrieval , and statistical language modeling .", "output": {"relations": {"used for": [{"head": {"text": "these", "start": 10, "end": 15}, "tail": {"text": "tasks", "start": 66, "end": 71}}], "hyponym of": [{"head": {"text": "speech and text understanding", "start": 82, "end": 111}, "tail": {"text": "tasks", "start": 66, "end": 71}}, {"head": {"text": "machine translation", "start": 114, "end": 133}, "tail": {"text": "tasks", "start": 66, "end": 71}}, {"head": {"text": "information retrieval", "start": 136, "end": 157}, "tail": {"text": "tasks", "start": 66, "end": 71}}, {"head": {"text": "statistical language modeling", "start": 164, "end": 193}, "tail": {"text": "tasks", "start": 66, "end": 71}}], "conjunction": [{"head": {"text": "speech and text understanding", "start": 82, "end": 111}, "tail": {"text": "machine translation", "start": 114, "end": 133}}, {"head": {"text": "machine translation", "start": 114, "end": 133}, "tail": {"text": "information retrieval", "start": 136, "end": 157}}, {"head": {"text": "information retrieval", "start": 136, "end": 157}, "tail": {"text": "statistical language modeling", "start": 164, "end": 193}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we evaluate the application of these segmen-tation algorithms to large vocabulary speech recognition using statistical n-gram language models based on the proposed word segments instead of entire words .", "output": {"relations": {"used for": [{"head": {"text": "segmen-tation algorithms", "start": 53, "end": 77}, "tail": {"text": "large vocabulary speech recognition", "start": 81, "end": 116}}], "evaluate for": [{"head": {"text": "statistical n-gram language models", "start": 123, "end": 157}, "tail": {"text": "segmen-tation algorithms", "start": 53, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments were done for two ag-glutinative and morphologically rich languages : Finnish and Turk-ish .", "output": {"relations": {"hyponym of": [{"head": {"text": "Finnish", "start": 82, "end": 89}, "tail": {"text": "ag-glutinative and morphologically rich languages", "start": 30, "end": 79}}, {"head": {"text": "Turk-ish", "start": 94, "end": 102}, "tail": {"text": "ag-glutinative and morphologically rich languages", "start": 30, "end": 79}}], "conjunction": [{"head": {"text": "Finnish", "start": 82, "end": 89}, "tail": {"text": "Turk-ish", "start": 94, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a recently collected spoken language corpus for the ATIS -LRB- Air Travel Information System -RRB- domain .", "output": {"relations": {"used for": [{"head": {"text": "spoken language corpus", "start": 42, "end": 64}, "tail": {"text": "ATIS -LRB- Air Travel Information System -RRB- domain", "start": 73, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We summarize the motivation for this effort , the goals , the implementation of a multi-site data collection paradigm , and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech , natural language and spoken language .", "output": {"relations": {"evaluate for": [{"head": {"text": "spontaneous speech", "start": 224, "end": 242}, "tail": {"text": "multi-site common evaluation of speech , natural language and spoken language", "start": 272, "end": 349}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes the Hierarchical Directed Acyclic Graph -LRB- HDAG -RRB- Kernel for structured natural language data .", "output": {"relations": {"used for": [{"head": {"text": "Hierarchical Directed Acyclic Graph -LRB- HDAG -RRB- Kernel", "start": 24, "end": 83}, "tail": {"text": "structured natural language data", "start": 88, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 24, "end": 30}, "tail": {"text": "question classification and sentence alignment tasks", "start": 34, "end": 86}}], "evaluate for": [{"head": {"text": "similarity measure", "start": 120, "end": 138}, "tail": {"text": "method", "start": 24, "end": 30}}, {"head": {"text": "kernel function", "start": 145, "end": 160}, "tail": {"text": "method", "start": 24, "end": 30}}], "conjunction": [{"head": {"text": "similarity measure", "start": 120, "end": 138}, "tail": {"text": "kernel function", "start": 145, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods .", "output": {"relations": {"compare": [{"head": {"text": "HDAG Kernel", "start": 52, "end": 63}, "tail": {"text": "kernel functions", "start": 85, "end": 101}}, {"head": {"text": "HDAG Kernel", "start": 52, "end": 63}, "tail": {"text": "baseline methods", "start": 106, "end": 122}}], "conjunction": [{"head": {"text": "kernel functions", "start": 85, "end": 101}, "tail": {"text": "baseline methods", "start": 106, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies .", "output": {"relations": {"used for": [{"head": {"text": "generative history-based latent variable model", "start": 81, "end": 127}, "tail": {"text": "CoNLL 2008 shared task", "start": 46, "end": 68}}, {"head": {"text": "generative history-based latent variable model", "start": 81, "end": 127}, "tail": {"text": "synchronous dependency parser", "start": 171, "end": 200}}, {"head": {"text": "synchronous dependency parser", "start": 171, "end": 200}, "tail": {"text": "syntactic and semantic dependencies", "start": 210, "end": 245}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The submitted model yields 79.1 % macro-average F1 performance , for the joint task , 86.9 % syntactic dependencies LAS and 71.0 % semantic dependencies F1 .", "output": {"relations": {"evaluate for": [{"head": {"text": "macro-average F1 performance", "start": 34, "end": 62}, "tail": {"text": "model", "start": 14, "end": 19}}, {"head": {"text": "macro-average F1 performance", "start": 34, "end": 62}, "tail": {"text": "task", "start": 79, "end": 83}}, {"head": {"text": "syntactic dependencies LAS", "start": 93, "end": 119}, "tail": {"text": "task", "start": 79, "end": 83}}, {"head": {"text": "semantic dependencies F1", "start": 131, "end": 155}, "tail": {"text": "task", "start": 79, "end": 83}}], "conjunction": [{"head": {"text": "syntactic dependencies LAS", "start": 93, "end": 119}, "tail": {"text": "semantic dependencies F1", "start": 131, "end": 155}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A larger model trained after the deadline achieves 80.5 % macro-average F1 , 87.6 % syntactic dependencies LAS , and 73.1 % semantic dependencies F1 .", "output": {"relations": {"evaluate for": [{"head": {"text": "macro-average F1", "start": 58, "end": 74}, "tail": {"text": "model", "start": 9, "end": 14}}, {"head": {"text": "syntactic dependencies LAS", "start": 84, "end": 110}, "tail": {"text": "model", "start": 9, "end": 14}}, {"head": {"text": "semantic dependencies F1", "start": 124, "end": 148}, "tail": {"text": "model", "start": 9, "end": 14}}], "conjunction": [{"head": {"text": "macro-average F1", "start": 58, "end": 74}, "tail": {"text": "syntactic dependencies LAS", "start": 84, "end": 110}}, {"head": {"text": "syntactic dependencies LAS", "start": 84, "end": 110}, "tail": {"text": "semantic dependencies F1", "start": 124, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an approach to annotating a level of discourse structure that is based on identifying discourse connectives and their arguments .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 14, "end": 22}, "tail": {"text": "discourse structure", "start": 48, "end": 67}}, {"head": {"text": "discourse connectives", "start": 97, "end": 118}, "tail": {"text": "approach", "start": 14, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The PDTB is being built directly on top of the Penn TreeBank and Propbank , thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms .", "output": {"relations": {"used for": [{"head": {"text": "PDTB", "start": 4, "end": 8}, "tail": {"text": "extraction of useful syntactic and semantic features", "start": 96, "end": 148}}, {"head": {"text": "Penn TreeBank", "start": 47, "end": 60}, "tail": {"text": "PDTB", "start": 4, "end": 8}}, {"head": {"text": "Propbank", "start": 65, "end": 73}, "tail": {"text": "PDTB", "start": 4, "end": 8}}], "evaluate for": [{"head": {"text": "PDTB", "start": 4, "end": 8}, "tail": {"text": "practical algorithms", "start": 220, "end": 240}}], "conjunction": [{"head": {"text": "Penn TreeBank", "start": 47, "end": 60}, "tail": {"text": "Propbank", "start": 65, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We provide a detailed preliminary analysis of inter-annotator agreement - both the level of agreement and the types of inter-annotator variation .", "output": {"relations": {"feature of": [{"head": {"text": "level of agreement", "start": 83, "end": 101}, "tail": {"text": "inter-annotator agreement", "start": 46, "end": 71}}, {"head": {"text": "inter-annotator variation", "start": 119, "end": 144}, "tail": {"text": "inter-annotator agreement", "start": 46, "end": 71}}], "conjunction": [{"head": {"text": "level of agreement", "start": 83, "end": 101}, "tail": {"text": "inter-annotator variation", "start": 119, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Currently , N-gram models are the most common and widely used models for statistical language modeling .", "output": {"relations": {"used for": [{"head": {"text": "N-gram models", "start": 12, "end": 25}, "tail": {"text": "statistical language modeling", "start": 73, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we investigated an alternative way to build language models , i.e. , using artificial neural networks to learn the language model .", "output": {"relations": {"used for": [{"head": {"text": "artificial neural networks", "start": 91, "end": 117}, "tail": {"text": "language model", "start": 60, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiment result shows that the neural network can learn a language model that has performance even better than standard statistical methods .", "output": {"relations": {"used for": [{"head": {"text": "neural network", "start": 37, "end": 51}, "tail": {"text": "language model", "start": 64, "end": 78}}], "compare": [{"head": {"text": "neural network", "start": 37, "end": 51}, "tail": {"text": "statistical methods", "start": 126, "end": 145}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Existing works in the field usually do not encode either the temporal evolution or the intensity of the observed facial displays .", "output": {"relations": {"conjunction": [{"head": {"text": "intensity of the observed facial displays", "start": 87, "end": 128}, "tail": {"text": "temporal evolution", "start": 61, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , intrinsic topology of multidimensional continuous facial affect data is first modeled by an ordinal man-ifold .", "output": {"relations": {"used for": [{"head": {"text": "ordinal man-ifold", "start": 108, "end": 125}, "tail": {"text": "intrinsic topology of multidimensional continuous facial", "start": 16, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This topology is then incorporated into the Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework for dynamic ordinal regression by constraining H-CORF parameters to lie on the ordinal manifold .", "output": {"relations": {"part of": [{"head": {"text": "topology", "start": 5, "end": 13}, "tail": {"text": "Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework", "start": 44, "end": 112}}], "used for": [{"head": {"text": "Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework", "start": 44, "end": 112}, "tail": {"text": "dynamic ordinal regression", "start": 117, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The resulting model attains simultaneous dynamic recognition and intensity estimation of facial expressions of multiple emotions .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 14, "end": 19}, "tail": {"text": "simultaneous dynamic recognition", "start": 28, "end": 60}}, {"head": {"text": "model", "start": 14, "end": 19}, "tail": {"text": "intensity estimation of facial expressions", "start": 65, "end": 107}}], "conjunction": [{"head": {"text": "simultaneous dynamic recognition", "start": 28, "end": 60}, "tail": {"text": "intensity estimation of facial expressions", "start": 65, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To the best of our knowledge , the proposed method is the first one to achieve this on both deliberate as well as spontaneous facial affect data .", "output": {"relations": {"evaluate for": [{"head": {"text": "spontaneous facial affect data", "start": 114, "end": 144}, "tail": {"text": "the proposed method", "start": 31, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recent advances in linear classification have shown that for applications such as document classification , the training can be extremely efficient .", "output": {"relations": {"hyponym of": [{"head": {"text": "document classification", "start": 82, "end": 105}, "tail": {"text": "applications", "start": 61, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These methods can not be easily applied to data larger than the memory capacity due to the random access to the disk .", "output": {"relations": {"compare": [{"head": {"text": "data", "start": 43, "end": 47}, "tail": {"text": "memory capacity", "start": 64, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose and analyze a block minimization framework for data larger than the memory size .", "output": {"relations": {"used for": [{"head": {"text": "block minimization framework", "start": 25, "end": 53}, "tail": {"text": "data", "start": 58, "end": 62}}], "compare": [{"head": {"text": "data", "start": 58, "end": 62}, "tail": {"text": "memory size", "start": 79, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We investigate two implementations of the proposed framework for primal and dual SVMs , respectively .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 51, "end": 60}, "tail": {"text": "primal and dual SVMs", "start": 65, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This in turn affects the accuracy of word sense disambiguation -LRB- WSD -RRB- systems trained and applied on different domains .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 25, "end": 33}, "tail": {"text": "word sense disambiguation -LRB- WSD -RRB- systems", "start": 37, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a method to estimate the sense priors of words drawn from a new domain , and highlights the importance of using well calibrated probabilities when performing these estimations .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "sense priors of words", "start": 45, "end": 66}}, {"head": {"text": "well calibrated probabilities", "start": 132, "end": 161}, "tail": {"text": "estimations", "start": 184, "end": 195}}], "feature of": [{"head": {"text": "new domain", "start": 80, "end": 90}, "tail": {"text": "sense priors of words", "start": 45, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By using well calibrated probabilities , we are able to estimate the sense priors effectively to achieve significant improvements in WSD accuracy .", "output": {"relations": {"used for": [{"head": {"text": "well calibrated probabilities", "start": 9, "end": 38}, "tail": {"text": "sense priors", "start": 69, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It was compiled from various resources such as encyclopedias and dictionaries , public databases of proper names and toponyms , collocations obtained from Czech WordNet , lists of botanical and zoological terms and others .", "output": {"relations": {"used for": [{"head": {"text": "encyclopedias", "start": 47, "end": 60}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "dictionaries", "start": 65, "end": 77}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "public databases of proper names and toponyms", "start": 80, "end": 125}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "collocations", "start": 128, "end": 140}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "lists of botanical and zoological terms", "start": 171, "end": 210}, "tail": {"text": "It", "start": 0, "end": 2}}], "conjunction": [{"head": {"text": "encyclopedias", "start": 47, "end": 60}, "tail": {"text": "dictionaries", "start": 65, "end": 77}}, {"head": {"text": "lists of botanical and zoological terms", "start": 171, "end": 210}, "tail": {"text": "collocations", "start": 128, "end": 140}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compare the built MWEs database with the corpus data from Czech National Corpus -LRB- approx .", "output": {"relations": {"used for": [{"head": {"text": "Czech National Corpus", "start": 61, "end": 82}, "tail": {"text": "MWEs database", "start": 21, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To obtain a more complete list of MWEs we propose and use a technique exploiting the Word Sketch Engine , which allows us to work with statistical parameters such as frequency of MWEs and their components as well as with the salience for the whole MWEs .", "output": {"relations": {"used for": [{"head": {"text": "technique", "start": 60, "end": 69}, "tail": {"text": "Word Sketch Engine", "start": 85, "end": 103}}], "feature of": [{"head": {"text": "statistical parameters", "start": 135, "end": 157}, "tail": {"text": "Word Sketch Engine", "start": 85, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also discuss exploitation of the database for working out a more adequate tagging and lemmatization .", "output": {"relations": {"used for": [{"head": {"text": "database", "start": 36, "end": 44}, "tail": {"text": "tagging", "start": 77, "end": 84}}, {"head": {"text": "database", "start": 36, "end": 44}, "tail": {"text": "lemmatization", "start": 89, "end": 102}}], "conjunction": [{"head": {"text": "tagging", "start": 77, "end": 84}, "tail": {"text": "lemmatization", "start": 89, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The final goal is to be able to recognize MWEs in corpus text and lemmatize them as complete lexical units , i. e. to make tagging and lemmatization more adequate .", "output": {"relations": {"used for": [{"head": {"text": "MWEs", "start": 42, "end": 46}, "tail": {"text": "tagging", "start": 123, "end": 130}}, {"head": {"text": "MWEs", "start": 42, "end": 46}, "tail": {"text": "lemmatization", "start": 135, "end": 148}}], "conjunction": [{"head": {"text": "tagging", "start": 123, "end": 130}, "tail": {"text": "lemmatization", "start": 135, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe the ongoing construction of a large , semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information , e.g. the construction of domain-independent lexica .", "output": {"relations": {"used for": [{"head": {"text": "semantically annotated corpus", "start": 50, "end": 79}, "tail": {"text": "large-scale acquisition of word-semantic information", "start": 115, "end": 167}}], "hyponym of": [{"head": {"text": "construction of domain-independent lexica", "start": 179, "end": 220}, "tail": {"text": "large-scale acquisition of word-semantic information", "start": 115, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The backbone of the annotation are semantic roles in the frame semantics paradigm .", "output": {"relations": {"part of": [{"head": {"text": "semantic roles", "start": 35, "end": 49}, "tail": {"text": "frame semantics paradigm", "start": 57, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "On this basis , we discuss the problems of vagueness and ambiguity in semantic annotation .", "output": {"relations": {"conjunction": [{"head": {"text": "vagueness", "start": 43, "end": 52}, "tail": {"text": "ambiguity", "start": 57, "end": 66}}], "feature of": [{"head": {"text": "vagueness", "start": 43, "end": 52}, "tail": {"text": "semantic annotation", "start": 70, "end": 89}}, {"head": {"text": "ambiguity", "start": 57, "end": 66}, "tail": {"text": "semantic annotation", "start": 70, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Statistical machine translation -LRB- SMT -RRB- is currently one of the hot spots in natural language processing .", "output": {"relations": {"hyponym of": [{"head": {"text": "Statistical machine translation -LRB- SMT -RRB-", "start": 0, "end": 47}, "tail": {"text": "natural language processing", "start": 85, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Over the last few years dramatic improvements have been made , and a number of comparative evaluations have shown , that SMT gives competitive results to rule-based translation systems , requiring significantly less development time .", "output": {"relations": {"compare": [{"head": {"text": "SMT", "start": 121, "end": 124}, "tail": {"text": "rule-based translation systems", "start": 154, "end": 184}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This is particularly important when building translation systems for new language pairs or new domains .", "output": {"relations": {"used for": [{"head": {"text": "translation systems", "start": 45, "end": 64}, "tail": {"text": "new language pairs", "start": 69, "end": 87}}, {"head": {"text": "translation systems", "start": 45, "end": 64}, "tail": {"text": "new domains", "start": 91, "end": 102}}], "conjunction": [{"head": {"text": "new language pairs", "start": 69, "end": 87}, "tail": {"text": "new domains", "start": 91, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "STTK , a statistical machine translation tool kit , will be introduced and used to build a working translation system .", "output": {"relations": {"hyponym of": [{"head": {"text": "STTK", "start": 0, "end": 4}, "tail": {"text": "statistical machine translation tool kit", "start": 9, "end": 49}}], "used for": [{"head": {"text": "STTK", "start": 0, "end": 4}, "tail": {"text": "translation system", "start": 99, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "STTK has been developed by the presenter and co-workers over a number of years and is currently used as the basis of CMU 's SMT system .", "output": {"relations": {"used for": [{"head": {"text": "STTK", "start": 0, "end": 4}, "tail": {"text": "SMT system", "start": 124, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It has also successfully been coupled with rule-based and example based machine translation modules to build a multi engine machine translation system .", "output": {"relations": {"conjunction": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "rule-based and example based machine translation modules", "start": 43, "end": 99}}], "used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "multi engine machine translation system", "start": 111, "end": 150}}, {"head": {"text": "rule-based and example based machine translation modules", "start": 43, "end": 99}, "tail": {"text": "multi engine machine translation system", "start": 111, "end": 150}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an unsupervised learning approach to building a non-English -LRB- Arabic -RRB- stemmer .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised learning approach", "start": 23, "end": 53}, "tail": {"text": "non-English -LRB- Arabic -RRB- stemmer", "start": 68, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The stemming model is based on statistical machine translation and it uses an English stemmer and a small -LRB- 10K sentences -RRB- parallel corpus as its sole training resources .", "output": {"relations": {"used for": [{"head": {"text": "statistical machine translation", "start": 31, "end": 62}, "tail": {"text": "stemming model", "start": 4, "end": 18}}, {"head": {"text": "English stemmer", "start": 78, "end": 93}, "tail": {"text": "it", "start": 67, "end": 69}}, {"head": {"text": "parallel corpus", "start": 132, "end": 147}, "tail": {"text": "it", "start": 67, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Monolingual , unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre .", "output": {"relations": {"used for": [{"head": {"text": "Monolingual , unannotated text", "start": 0, "end": 30}, "tail": {"text": "stemmer", "start": 66, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our resource-frugal approach results in 87.5 % agreement with a state of the art , proprietary Arabic stemmer built using rules , affix lists , and human annotated text , in addition to an unsupervised component .", "output": {"relations": {"compare": [{"head": {"text": "resource-frugal approach", "start": 4, "end": 28}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}], "evaluate for": [{"head": {"text": "agreement", "start": 47, "end": 56}, "tail": {"text": "resource-frugal approach", "start": 4, "end": 28}}, {"head": {"text": "agreement", "start": 47, "end": 56}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}], "used for": [{"head": {"text": "rules", "start": 122, "end": 127}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}, {"head": {"text": "affix lists", "start": 130, "end": 141}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}, {"head": {"text": "human annotated text", "start": 148, "end": 168}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}, {"head": {"text": "unsupervised component", "start": 189, "end": 211}, "tail": {"text": "Arabic stemmer", "start": 95, "end": 109}}], "conjunction": [{"head": {"text": "rules", "start": 122, "end": 127}, "tail": {"text": "affix lists", "start": 130, "end": 141}}, {"head": {"text": "affix lists", "start": 130, "end": 141}, "tail": {"text": "human annotated text", "start": 148, "end": 168}}, {"head": {"text": "human annotated text", "start": 148, "end": 168}, "tail": {"text": "unsupervised component", "start": 189, "end": 211}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38 % in average precision over unstemmed text , and 96 % of the performance of the proprietary stemmer above .", "output": {"relations": {"used for": [{"head": {"text": "Arabic information retrieval", "start": 28, "end": 56}, "tail": {"text": "Task-based evaluation", "start": 0, "end": 21}}], "evaluate for": [{"head": {"text": "average precision", "start": 96, "end": 113}, "tail": {"text": "Task-based evaluation", "start": 0, "end": 21}}, {"head": {"text": "average precision", "start": 96, "end": 113}, "tail": {"text": "unstemmed text", "start": 119, "end": 133}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper assesses the capability of an HMM-based TTS system to produce German speech .", "output": {"relations": {"used for": [{"head": {"text": "HMM-based TTS system", "start": 40, "end": 60}, "tail": {"text": "German speech", "start": 72, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In addition , the system is adapted to a small set of football announcements , in an exploratory attempt to synthe-sise expressive speech .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 18, "end": 24}, "tail": {"text": "football announcements", "start": 54, "end": 76}}, {"head": {"text": "system", "start": 18, "end": 24}, "tail": {"text": "expressive speech", "start": 120, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We conclude that the HMMs are able to produce highly intelligible neutral German speech , with a stable quality , and that the expressivity is partially captured in spite of the small size of the football dataset .", "output": {"relations": {"used for": [{"head": {"text": "HMMs", "start": 21, "end": 25}, "tail": {"text": "intelligible neutral German speech", "start": 53, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , in contrast to the approach of Dalrymple et al. -LSB- 1991 -RSB- , the treatment directly encodes the intuitive distinction between full NPs and the referential elements that corefer with them through what we term role linking .", "output": {"relations": {"conjunction": [{"head": {"text": "full NPs", "start": 146, "end": 154}, "tail": {"text": "referential elements", "start": 163, "end": 183}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , the analysis extends directly to other discourse copying phenomena .", "output": {"relations": {"used for": [{"head": {"text": "analysis", "start": 14, "end": 22}, "tail": {"text": "discourse copying phenomena", "start": 49, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "How to obtain hierarchical relations -LRB- e.g. superordinate - hyponym relation , synonym relation -RRB- is one of the most important problems for thesaurus construction .", "output": {"relations": {"part of": [{"head": {"text": "hierarchical relations", "start": 14, "end": 36}, "tail": {"text": "thesaurus construction", "start": 148, "end": 170}}], "hyponym of": [{"head": {"text": "superordinate - hyponym relation", "start": 48, "end": 80}, "tail": {"text": "hierarchical relations", "start": 14, "end": 36}}, {"head": {"text": "synonym relation", "start": 83, "end": 99}, "tail": {"text": "hierarchical relations", "start": 14, "end": 36}}], "conjunction": [{"head": {"text": "superordinate - hyponym relation", "start": 48, "end": 80}, "tail": {"text": "synonym relation", "start": 83, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A pilot system for extracting these relations automatically from an ordinary Japanese language dictionary -LRB- Shinmeikai Kokugojiten , published by Sansei-do , in machine readable form -RRB- is given .", "output": {"relations": {"used for": [{"head": {"text": "Japanese language dictionary", "start": 77, "end": 105}, "tail": {"text": "relations", "start": 36, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The features of the definition sentences in the dictionary , the mechanical extraction of the hierarchical relations and the estimation of the results are discussed .", "output": {"relations": {"used for": [{"head": {"text": "definition sentences", "start": 20, "end": 40}, "tail": {"text": "features", "start": 4, "end": 12}}], "part of": [{"head": {"text": "definition sentences", "start": 20, "end": 40}, "tail": {"text": "dictionary", "start": 48, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This is evident most compellingly by the very low recognition rate of all existing face recognition systems when applied to live CCTV camera input .", "output": {"relations": {"evaluate for": [{"head": {"text": "recognition rate", "start": 50, "end": 66}, "tail": {"text": "face recognition systems", "start": 83, "end": 107}}], "used for": [{"head": {"text": "live CCTV camera input", "start": 124, "end": 146}, "tail": {"text": "face recognition systems", "start": 83, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a Bayesian framework to perform multi-modal -LRB- such as variations in viewpoint and illumination -RRB- face image super-resolution for recognition in tensor space .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian framework", "start": 29, "end": 47}, "tail": {"text": "face image super-resolution", "start": 132, "end": 159}}, {"head": {"text": "face image super-resolution", "start": 132, "end": 159}, "tail": {"text": "recognition", "start": 164, "end": 175}}], "conjunction": [{"head": {"text": "viewpoint", "start": 99, "end": 108}, "tail": {"text": "illumination", "start": 113, "end": 125}}], "feature of": [{"head": {"text": "tensor space", "start": 179, "end": 191}, "tail": {"text": "recognition", "start": 164, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Given a single modal low-resolution face image , we benefit from the multiple factor interactions of training tensor , and super-resolve its high-resolution reconstructions across different modalities for face recognition .", "output": {"relations": {"used for": [{"head": {"text": "single modal low-resolution face image", "start": 8, "end": 46}, "tail": {"text": "high-resolution reconstructions", "start": 141, "end": 172}}, {"head": {"text": "multiple factor interactions of training tensor", "start": 69, "end": 116}, "tail": {"text": "high-resolution reconstructions", "start": 141, "end": 172}}, {"head": {"text": "high-resolution reconstructions", "start": 141, "end": 172}, "tail": {"text": "face recognition", "start": 205, "end": 221}}], "feature of": [{"head": {"text": "modalities", "start": 190, "end": 200}, "tail": {"text": "high-resolution reconstructions", "start": 141, "end": 172}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Instead of performing pixel-domain super-resolution and recognition independently as two separate sequential processes , we integrate the tasks of super-resolution and recognition by directly computing a maximum likelihood identity parameter vector in high-resolution tensor space for recognition .", "output": {"relations": {"hyponym of": [{"head": {"text": "super-resolution", "start": 35, "end": 51}, "tail": {"text": "pixel-domain super-resolution and recognition", "start": 22, "end": 67}}, {"head": {"text": "recognition", "start": 56, "end": 67}, "tail": {"text": "pixel-domain super-resolution and recognition", "start": 22, "end": 67}}], "conjunction": [{"head": {"text": "super-resolution", "start": 35, "end": 51}, "tail": {"text": "recognition", "start": 56, "end": 67}}], "used for": [{"head": {"text": "maximum likelihood identity parameter vector", "start": 204, "end": 248}, "tail": {"text": "super-resolution", "start": 35, "end": 51}}, {"head": {"text": "maximum likelihood identity parameter vector", "start": 204, "end": 248}, "tail": {"text": "recognition", "start": 56, "end": 67}}, {"head": {"text": "maximum likelihood identity parameter vector", "start": 204, "end": 248}, "tail": {"text": "recognition", "start": 56, "end": 67}}], "feature of": [{"head": {"text": "high-resolution tensor space", "start": 252, "end": 280}, "tail": {"text": "maximum likelihood identity parameter vector", "start": 204, "end": 248}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show results from multi-modal super-resolution and face recognition experiments across different imaging modalities , using low-resolution images as testing inputs and demonstrate improved recognition rates over standard tensorface and eigenface representations .", "output": {"relations": {"used for": [{"head": {"text": "low-resolution images", "start": 127, "end": 148}, "tail": {"text": "multi-modal super-resolution and face recognition", "start": 21, "end": 70}}], "evaluate for": [{"head": {"text": "recognition rates", "start": 192, "end": 209}, "tail": {"text": "multi-modal super-resolution and face recognition", "start": 21, "end": 70}}, {"head": {"text": "recognition rates", "start": 192, "end": 209}, "tail": {"text": "tensorface and eigenface representations", "start": 224, "end": 264}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models .", "output": {"relations": {"used for": [{"head": {"text": "phrase-based unigram model", "start": 30, "end": 56}, "tail": {"text": "statistical machine translation", "start": 61, "end": 92}}, {"head": {"text": "model parameters", "start": 125, "end": 141}, "tail": {"text": "phrase-based unigram model", "start": 30, "end": 56}}], "compare": [{"head": {"text": "phrase-based unigram model", "start": 30, "end": 56}, "tail": {"text": "phrase-based models", "start": 155, "end": 174}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "During decoding , we use a block unigram model and a word-based trigram language model .", "output": {"relations": {"used for": [{"head": {"text": "block unigram model", "start": 27, "end": 46}, "tail": {"text": "decoding", "start": 7, "end": 15}}, {"head": {"text": "word-based trigram language model", "start": 53, "end": 86}, "tail": {"text": "decoding", "start": 7, "end": 15}}], "conjunction": [{"head": {"text": "word-based trigram language model", "start": 53, "end": 86}, "tail": {"text": "block unigram model", "start": 27, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "During training , the blocks are learned from source interval projections using an underlying word alignment .", "output": {"relations": {"used for": [{"head": {"text": "source interval projections", "start": 46, "end": 73}, "tail": {"text": "blocks", "start": 22, "end": 28}}, {"head": {"text": "word alignment", "start": 94, "end": 108}, "tail": {"text": "source interval projections", "start": 46, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show experimental results on block selection criteria based on unigram counts and phrase length .", "output": {"relations": {"used for": [{"head": {"text": "unigram counts", "start": 66, "end": 80}, "tail": {"text": "block selection criteria", "start": 32, "end": 56}}, {"head": {"text": "phrase length", "start": 85, "end": 98}, "tail": {"text": "block selection criteria", "start": 32, "end": 56}}], "conjunction": [{"head": {"text": "unigram counts", "start": 66, "end": 80}, "tail": {"text": "phrase length", "start": 85, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed -LRB- e.g. face detection or database retrieval -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 26, "end": 34}, "tail": {"text": "fast detection", "start": 49, "end": 63}}], "conjunction": [{"head": {"text": "face detection", "start": 160, "end": 174}, "tail": {"text": "database retrieval", "start": 178, "end": 196}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In such domains a cascade of simple classifiers each trained to achieve high detection rates and modest false positive rates can yield a final detector with many desirable features : including high detection rates , very low false positive rates , and fast performance .", "output": {"relations": {"used for": [{"head": {"text": "cascade of simple classifiers", "start": 18, "end": 47}, "tail": {"text": "detector", "start": 143, "end": 151}}], "evaluate for": [{"head": {"text": "detection rates", "start": 77, "end": 92}, "tail": {"text": "classifiers", "start": 36, "end": 47}}, {"head": {"text": "modest false positive rates", "start": 97, "end": 124}, "tail": {"text": "classifiers", "start": 36, "end": 47}}], "conjunction": [{"head": {"text": "detection rates", "start": 77, "end": 92}, "tail": {"text": "modest false positive rates", "start": 97, "end": 124}}], "feature of": [{"head": {"text": "features", "start": 172, "end": 180}, "tail": {"text": "detector", "start": 143, "end": 151}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Achieving extremely high detection rates , rather than low error , is not a task typically addressed by machine learning algorithms .", "output": {"relations": {"compare": [{"head": {"text": "detection rates", "start": 25, "end": 40}, "tail": {"text": "low error", "start": 55, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a new variant of AdaBoost as a mechanism for training the simple classifiers used in the cascade .", "output": {"relations": {"used for": [{"head": {"text": "AdaBoost", "start": 28, "end": 36}, "tail": {"text": "classifiers", "start": 76, "end": 87}}, {"head": {"text": "classifiers", "start": 76, "end": 87}, "tail": {"text": "cascade", "start": 100, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results in the domain of face detection show the training algorithm yields significant improvements in performance over conventional AdaBoost .", "output": {"relations": {"used for": [{"head": {"text": "training algorithm", "start": 62, "end": 80}, "tail": {"text": "face detection", "start": 38, "end": 52}}, {"head": {"text": "AdaBoost", "start": 146, "end": 154}, "tail": {"text": "face detection", "start": 38, "end": 52}}], "compare": [{"head": {"text": "AdaBoost", "start": 146, "end": 154}, "tail": {"text": "training algorithm", "start": 62, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The final face detection system can process 15 frames per second , achieves over 90 % detection , and a false positive rate of 1 in a 1,000,000 .", "output": {"relations": {"conjunction": [{"head": {"text": "detection", "start": 15, "end": 24}, "tail": {"text": "false positive rate", "start": 104, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a method for learning joint embed-dings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "joint embed-dings of images and text", "start": 42, "end": 78}}, {"head": {"text": "two-branch neural network", "start": 87, "end": 112}, "tail": {"text": "method", "start": 22, "end": 28}}], "part of": [{"head": {"text": "multiple layers of linear projections", "start": 118, "end": 155}, "tail": {"text": "two-branch neural network", "start": 87, "end": 112}}, {"head": {"text": "nonlinearities", "start": 168, "end": 182}, "tail": {"text": "two-branch neural network", "start": 87, "end": 112}}], "conjunction": [{"head": {"text": "multiple layers of linear projections", "start": 118, "end": 155}, "tail": {"text": "nonlinearities", "start": 168, "end": 182}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The network is trained using a large-margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature .", "output": {"relations": {"used for": [{"head": {"text": "large-margin objective", "start": 31, "end": 53}, "tail": {"text": "network", "start": 4, "end": 11}}], "feature of": [{"head": {"text": "cross-view ranking constraints", "start": 68, "end": 98}, "tail": {"text": "large-margin objective", "start": 31, "end": 53}}, {"head": {"text": "within-view neighborhood structure preservation constraints", "start": 104, "end": 163}, "tail": {"text": "large-margin objective", "start": 31, "end": 53}}], "conjunction": [{"head": {"text": "cross-view ranking constraints", "start": 68, "end": 98}, "tail": {"text": "within-view neighborhood structure preservation constraints", "start": 104, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and text-to-image retrieval .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 79, "end": 87}, "tail": {"text": "approach", "start": 36, "end": 44}}, {"head": {"text": "image-to-text and text-to-image retrieval", "start": 92, "end": 133}, "tail": {"text": "approach", "start": 36, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase lo-calization on the Flickr30K Entities dataset .", "output": {"relations": {"evaluate for": [{"head": {"text": "Flickr30K and MSCOCO image-sentence datasets", "start": 56, "end": 100}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "phrase lo-calization", "start": 138, "end": 158}, "tail": {"text": "method", "start": 4, "end": 10}}], "used for": [{"head": {"text": "Flickr30K Entities dataset", "start": 166, "end": 192}, "tail": {"text": "phrase lo-calization", "start": 138, "end": 158}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We investigate that claim by adopting a simple MT-based paraphrasing technique and evaluating QA system performance on paraphrased questions .", "output": {"relations": {"used for": [{"head": {"text": "MT-based paraphrasing technique", "start": 47, "end": 78}, "tail": {"text": "QA system", "start": 94, "end": 103}}], "evaluate for": [{"head": {"text": "paraphrased questions", "start": 119, "end": 140}, "tail": {"text": "QA system", "start": 94, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The TAP-XL Automated Analyst 's Assistant is an application designed to help an English - speaking analyst write a topical report , culling information from a large inflow of multilingual , multimedia data .", "output": {"relations": {"used for": [{"head": {"text": "multilingual , multimedia data", "start": 175, "end": 205}, "tail": {"text": "TAP-XL Automated Analyst 's Assistant", "start": 4, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It gives users the ability to spend their time finding more data relevant to their task , and gives them translingual reach into other languages by leveraging human language technology .", "output": {"relations": {"used for": [{"head": {"text": "human language technology", "start": 159, "end": 184}, "tail": {"text": "It", "start": 0, "end": 2}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper discusses the application of Unification Categorial Grammar -LRB- UCG -RRB- to the framework of Isomorphic Grammars for Machine Translation pioneered by Landsbergen .", "output": {"relations": {"used for": [{"head": {"text": "Unification Categorial Grammar -LRB- UCG -RRB-", "start": 40, "end": 86}, "tail": {"text": "Isomorphic Grammars", "start": 107, "end": 126}}, {"head": {"text": "Isomorphic Grammars", "start": 107, "end": 126}, "tail": {"text": "Machine Translation", "start": 131, "end": 150}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Isomorphic Grammars approach to MT involves developing the grammars of the Source and Target languages in parallel , in order to ensure that SL and TL expressions which stand in the translation relation have isomorphic derivations .", "output": {"relations": {"used for": [{"head": {"text": "Isomorphic Grammars approach", "start": 4, "end": 32}, "tail": {"text": "MT", "start": 36, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After introducing this approach to MT system design , and the basics of monolingual UCG , we will show how the two can be integrated , and present an example from an implemented bi-directional English-Spanish fragment .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "MT system design", "start": 35, "end": 51}}, {"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "monolingual UCG", "start": 72, "end": 87}}], "conjunction": [{"head": {"text": "MT system design", "start": 35, "end": 51}, "tail": {"text": "monolingual UCG", "start": 72, "end": 87}}], "hyponym of": [{"head": {"text": "MT system design", "start": 35, "end": 51}, "tail": {"text": "two", "start": 111, "end": 114}}, {"head": {"text": "monolingual UCG", "start": 72, "end": 87}, "tail": {"text": "two", "start": 111, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the security domain a key problem is identifying rare behaviours of interest .", "output": {"relations": {"part of": [{"head": {"text": "identifying rare behaviours of interest", "start": 40, "end": 79}, "tail": {"text": "security domain", "start": 7, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Training examples for these behaviours may or may not exist , and if they do exist there will be few examples , quite probably one .", "output": {"relations": {"used for": [{"head": {"text": "Training examples", "start": 0, "end": 17}, "tail": {"text": "behaviours", "start": 28, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a novel weakly supervised algorithm that can detect behaviours that either have never before been seen or for which there are few examples .", "output": {"relations": {"used for": [{"head": {"text": "weakly supervised algorithm", "start": 19, "end": 46}, "tail": {"text": "behaviours", "start": 63, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Global context is modelled , allowing the detection of abnormal behaviours that in isolation appear normal .", "output": {"relations": {"used for": [{"head": {"text": "Global context", "start": 0, "end": 14}, "tail": {"text": "detection of abnormal behaviours", "start": 42, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have developed a computational model of the process of describing the layout of an apartment or house , a much-studied discourse task first characterized linguistically by Linde -LRB- 1974 -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "computational model", "start": 20, "end": 39}, "tail": {"text": "discourse task", "start": 122, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is embodied in a program , APT , that can reproduce segments of actual tape-recorded descriptions , using organizational and discourse strategies derived through analysis of our corpus .", "output": {"relations": {"part of": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "program", "start": 27, "end": 34}}], "used for": [{"head": {"text": "organizational and discourse strategies", "start": 116, "end": 155}, "tail": {"text": "APT", "start": 37, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 32, "end": 40}, "tail": {"text": "Thai key prediction", "start": 96, "end": 115}}, {"head": {"text": "approach", "start": 32, "end": 40}, "tail": {"text": "Thai-English language identification", "start": 120, "end": 156}}, {"head": {"text": "n-gram models", "start": 51, "end": 64}, "tail": {"text": "approach", "start": 32, "end": 40}}, {"head": {"text": "error-correction rules", "start": 69, "end": 91}, "tail": {"text": "approach", "start": 32, "end": 40}}], "conjunction": [{"head": {"text": "n-gram models", "start": 51, "end": 64}, "tail": {"text": "error-correction rules", "start": 69, "end": 91}}, {"head": {"text": "Thai key prediction", "start": 96, "end": 115}, "tail": {"text": "Thai-English language identification", "start": 120, "end": 156}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules .", "output": {"relations": {"used for": [{"head": {"text": "mutual information", "start": 58, "end": 76}, "tail": {"text": "rule-reduction algorithm", "start": 24, "end": 48}}, {"head": {"text": "mutual information", "start": 58, "end": 76}, "tail": {"text": "error-correction rules", "start": 91, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our algorithm reported more than 99 % accuracy in both language identification and key prediction .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 4, "end": 13}, "tail": {"text": "language identification", "start": 55, "end": 78}}, {"head": {"text": "algorithm", "start": 4, "end": 13}, "tail": {"text": "key prediction", "start": 83, "end": 97}}], "evaluate for": [{"head": {"text": "accuracy", "start": 38, "end": 46}, "tail": {"text": "algorithm", "start": 4, "end": 13}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper concerns the discourse understanding process in spoken dialogue systems .", "output": {"relations": {"used for": [{"head": {"text": "discourse understanding process", "start": 24, "end": 55}, "tail": {"text": "spoken dialogue systems", "start": 59, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This process enables the system to understand user utterances based on the context of a dialogue .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 25, "end": 31}, "tail": {"text": "user utterances", "start": 46, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "ambiguity", "start": 48, "end": 57}}, {"head": {"text": "statistical information", "start": 67, "end": 90}, "tail": {"text": "method", "start": 22, "end": 28}}, {"head": {"text": "dialogue corpora", "start": 105, "end": 121}, "tail": {"text": "statistical information", "start": 67, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unlike conventional methods that use hand-crafted rules , the proposed method enables easy design of the discourse understanding process .", "output": {"relations": {"used for": [{"head": {"text": "hand-crafted rules", "start": 37, "end": 55}, "tail": {"text": "methods", "start": 20, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 37, "end": 43}, "tail": {"text": "method", "start": 71, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time .", "output": {"relations": {"used for": [{"head": {"text": "news articles", "start": 76, "end": 89}, "tail": {"text": "question-focused sentence retrieval", "start": 27, "end": 62}}], "feature of": [{"head": {"text": "multi-event stories", "start": 101, "end": 120}, "tail": {"text": "news articles", "start": 76, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To address the sentence retrieval problem , we apply a stochastic , graph-based method for comparing the relative importance of the textual units , which was previously used successfully for generic summarization .", "output": {"relations": {"used for": [{"head": {"text": "stochastic , graph-based method", "start": 55, "end": 86}, "tail": {"text": "sentence retrieval problem", "start": 15, "end": 41}}, {"head": {"text": "stochastic , graph-based method", "start": 55, "end": 86}, "tail": {"text": "generic summarization", "start": 191, "end": 212}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline , which compares the similarity of each sentence to the input question via IDF-weighted word overlap .", "output": {"relations": {"compare": [{"head": {"text": "baseline", "start": 116, "end": 124}, "tail": {"text": "it", "start": 35, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our experiments , the method achieves a TRDR score that is significantly higher than that of the baseline .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 25, "end": 31}, "tail": {"text": "baseline", "start": 100, "end": 108}}], "evaluate for": [{"head": {"text": "TRDR score", "start": 43, "end": 53}, "tail": {"text": "method", "start": 25, "end": 31}}, {"head": {"text": "TRDR score", "start": 43, "end": 53}, "tail": {"text": "baseline", "start": 100, "end": 108}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes that sentence analysis should be treated as defeasible reasoning , and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige , which is a formalization of defeasible reasoning , that includes arguments and defeat rules that capture defeasibility .", "output": {"relations": {"used for": [{"head": {"text": "defeasible reasoning", "start": 64, "end": 84}, "tail": {"text": "sentence analysis", "start": 25, "end": 42}}, {"head": {"text": "treatment", "start": 107, "end": 116}, "tail": {"text": "Japanese sentence analyses", "start": 121, "end": 147}}, {"head": {"text": "argumentation system", "start": 157, "end": 177}, "tail": {"text": "Japanese sentence analyses", "start": 121, "end": 147}}], "part of": [{"head": {"text": "arguments", "start": 257, "end": 266}, "tail": {"text": "formalization of defeasible reasoning", "start": 203, "end": 240}}, {"head": {"text": "defeat rules", "start": 271, "end": 283}, "tail": {"text": "formalization of defeasible reasoning", "start": 203, "end": 240}}], "conjunction": [{"head": {"text": "arguments", "start": 257, "end": 266}, "tail": {"text": "defeat rules", "start": 271, "end": 283}}], "feature of": [{"head": {"text": "defeasibility", "start": 297, "end": 310}, "tail": {"text": "arguments", "start": 257, "end": 266}}, {"head": {"text": "defeasibility", "start": 297, "end": 310}, "tail": {"text": "defeat rules", "start": 271, "end": 283}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It gives an overview of methods used for visual speech animation , parameterization of a human face and a tongue , necessary data sources and a synthesis method .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 24, "end": 31}, "tail": {"text": "visual speech animation", "start": 41, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A 3D animation model is used for a pseudo-muscular animation schema to create such animation of visual speech which is usable for a lipreading .", "output": {"relations": {"used for": [{"head": {"text": "3D animation model", "start": 2, "end": 20}, "tail": {"text": "pseudo-muscular animation schema", "start": 35, "end": 67}}, {"head": {"text": "pseudo-muscular animation schema", "start": 35, "end": 67}, "tail": {"text": "animation of visual speech", "start": 83, "end": 109}}, {"head": {"text": "animation of visual speech", "start": 83, "end": 109}, "tail": {"text": "lipreading", "start": 132, "end": 142}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , a problem of forming articulatory trajectories is formulated to solve labial coarticulation effects .", "output": {"relations": {"used for": [{"head": {"text": "forming articulatory trajectories", "start": 27, "end": 60}, "tail": {"text": "labial coarticulation effects", "start": 84, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is used for the synthesis method based on a selection of articulatory targets and interpolation technique .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "synthesis method", "start": 19, "end": 35}}, {"head": {"text": "selection of articulatory targets", "start": 47, "end": 80}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "interpolation technique", "start": 85, "end": 108}, "tail": {"text": "It", "start": 0, "end": 2}}], "conjunction": [{"head": {"text": "selection of articulatory targets", "start": 47, "end": 80}, "tail": {"text": "interpolation technique", "start": 85, "end": 108}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , our experience with TACITUS ; especially in the MUC-3 evaluation , has shown that principled techniques for syntactic and pragmatic analysis can be bolstered with methods for achieving robustness .", "output": {"relations": {"used for": [{"head": {"text": "techniques", "start": 103, "end": 113}, "tail": {"text": "syntactic and pragmatic analysis", "start": 118, "end": 150}}], "evaluate for": [{"head": {"text": "robustness", "start": 195, "end": 205}, "tail": {"text": "methods", "start": 173, "end": 180}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe three techniques for making syntactic analysis more robust -- an agenda-based scheduling parser , a recovery technique for failed parses , and a new technique called terminal substring parsing .", "output": {"relations": {"used for": [{"head": {"text": "three techniques", "start": 12, "end": 28}, "tail": {"text": "syntactic analysis", "start": 40, "end": 58}}, {"head": {"text": "recovery technique", "start": 112, "end": 130}, "tail": {"text": "syntactic analysis", "start": 40, "end": 58}}, {"head": {"text": "recovery technique", "start": 112, "end": 130}, "tail": {"text": "failed parses", "start": 135, "end": 148}}], "hyponym of": [{"head": {"text": "agenda-based scheduling parser", "start": 77, "end": 107}, "tail": {"text": "three techniques", "start": 12, "end": 28}}, {"head": {"text": "recovery technique", "start": 112, "end": 130}, "tail": {"text": "three techniques", "start": 12, "end": 28}}, {"head": {"text": "technique", "start": 18, "end": 27}, "tail": {"text": "three techniques", "start": 12, "end": 28}}], "conjunction": [{"head": {"text": "agenda-based scheduling parser", "start": 77, "end": 107}, "tail": {"text": "recovery technique", "start": 112, "end": 130}}, {"head": {"text": "recovery technique", "start": 112, "end": 130}, "tail": {"text": "technique", "start": 18, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For pragmatics processing , we describe how the method of abductive inference is inherently robust , in that an interpretation is always possible , so that in the absence of the required world knowledge , performance degrades gracefully .", "output": {"relations": {"used for": [{"head": {"text": "abductive inference", "start": 58, "end": 77}, "tail": {"text": "pragmatics processing", "start": 4, "end": 25}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a Hidden Markov Model -LRB- HMM -RRB- and an HMM-based chunk tagger , from which a named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system is built to recognize and classify names , times and numerical quantities .", "output": {"relations": {"conjunction": [{"head": {"text": "Hidden Markov Model -LRB- HMM -RRB-", "start": 22, "end": 57}, "tail": {"text": "HMM-based chunk tagger", "start": 65, "end": 87}}, {"head": {"text": "names", "start": 201, "end": 206}, "tail": {"text": "times and numerical quantities", "start": 209, "end": 239}}], "used for": [{"head": {"text": "Hidden Markov Model -LRB- HMM -RRB-", "start": 22, "end": 57}, "tail": {"text": "named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system", "start": 103, "end": 165}}, {"head": {"text": "HMM-based chunk tagger", "start": 65, "end": 87}, "tail": {"text": "named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system", "start": 103, "end": 165}}, {"head": {"text": "named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system", "start": 103, "end": 165}, "tail": {"text": "names", "start": 201, "end": 206}}, {"head": {"text": "named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system", "start": 103, "end": 165}, "tail": {"text": "times and numerical quantities", "start": 209, "end": 239}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Through the HMM , our system is able to apply and integrate four types of internal and external evidences : 1 -RRB- simple deterministic internal feature of the words , such as capitalization and digitalization ; 2 -RRB- internal semantic feature of important triggers ; 3 -RRB- internal gazetteer feature ; 4 -RRB- external macro context feature .", "output": {"relations": {"hyponym of": [{"head": {"text": "capitalization", "start": 177, "end": 191}, "tail": {"text": "deterministic internal feature of the words", "start": 123, "end": 166}}, {"head": {"text": "digitalization", "start": 196, "end": 210}, "tail": {"text": "deterministic internal feature of the words", "start": 123, "end": 166}}], "conjunction": [{"head": {"text": "capitalization", "start": 177, "end": 191}, "tail": {"text": "digitalization", "start": 196, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6 % and 94.1 % respectively .", "output": {"relations": {"evaluate for": [{"head": {"text": "MUC-6 and MUC-7 English NE tasks", "start": 28, "end": 60}, "tail": {"text": "system", "start": 18, "end": 24}}, {"head": {"text": "F-measures", "start": 70, "end": 80}, "tail": {"text": "system", "start": 18, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Two themes have evolved in speech and text image processing work at Xerox PARC that expand and redefine the role of recognition technology in document-oriented applications .", "output": {"relations": {"part of": [{"head": {"text": "themes", "start": 4, "end": 10}, "tail": {"text": "speech and text image processing", "start": 27, "end": 59}}], "used for": [{"head": {"text": "recognition technology", "start": 116, "end": 138}, "tail": {"text": "document-oriented applications", "start": 142, "end": 172}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One is the development of systems that provide functionality similar to that of text processors but operate directly on audio and scanned image data .", "output": {"relations": {"conjunction": [{"head": {"text": "systems", "start": 26, "end": 33}, "tail": {"text": "text processors", "start": 80, "end": 95}}], "used for": [{"head": {"text": "audio and scanned image data", "start": 120, "end": 148}, "tail": {"text": "systems", "start": 26, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A second , related theme is the use of speech and text-image recognition to retrieve arbitrary , user-specified information from documents with signal content .", "output": {"relations": {"used for": [{"head": {"text": "speech and text-image recognition", "start": 39, "end": 72}, "tail": {"text": "theme", "start": 19, "end": 24}}, {"head": {"text": "documents with signal content", "start": 129, "end": 158}, "tail": {"text": "speech and text-image recognition", "start": 39, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper discusses three research initiatives at PARC that exemplify these themes : a text-image editor -LSB- 1 -RSB- , a wordspotter for voice editing and indexing -LSB- 12 -RSB- , and a decoding framework for scanned-document content retrieval -LSB- 4 -RSB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "text-image editor", "start": 88, "end": 105}, "tail": {"text": "research", "start": 27, "end": 35}}, {"head": {"text": "wordspotter", "start": 124, "end": 135}, "tail": {"text": "research", "start": 27, "end": 35}}, {"head": {"text": "decoding framework", "start": 190, "end": 208}, "tail": {"text": "research", "start": 27, "end": 35}}], "conjunction": [{"head": {"text": "text-image editor", "start": 88, "end": 105}, "tail": {"text": "wordspotter", "start": 124, "end": 135}}, {"head": {"text": "wordspotter", "start": 124, "end": 135}, "tail": {"text": "voice editing and indexing", "start": 140, "end": 166}}, {"head": {"text": "voice editing and indexing", "start": 140, "end": 166}, "tail": {"text": "decoding framework", "start": 190, "end": 208}}], "used for": [{"head": {"text": "decoding framework", "start": 190, "end": 208}, "tail": {"text": "scanned-document content retrieval", "start": 213, "end": 247}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The problem of predicting image or video interestingness from their low-level feature representations has received increasing interest .", "output": {"relations": {"used for": [{"head": {"text": "low-level feature representations", "start": 68, "end": 101}, "tail": {"text": "predicting image or video interestingness", "start": 15, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To make the annotation less subjective and more reliable , recent studies employ crowdsourcing tools to collect pairwise comparisons -- relying on majority voting to prune the annotation outliers/errors .", "output": {"relations": {"used for": [{"head": {"text": "crowdsourcing tools", "start": 81, "end": 100}, "tail": {"text": "pairwise comparisons", "start": 112, "end": 132}}, {"head": {"text": "majority voting", "start": 147, "end": 162}, "tail": {"text": "crowdsourcing tools", "start": 81, "end": 100}}, {"head": {"text": "majority voting", "start": 147, "end": 162}, "tail": {"text": "annotation outliers/errors", "start": 176, "end": 202}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a more principled way to identify annotation outliers by formulating the interestingness prediction task as a unified robust learning to rank problem , tackling both the outlier detection and interestingness prediction tasks jointly .", "output": {"relations": {"used for": [{"head": {"text": "way", "start": 45, "end": 48}, "tail": {"text": "annotation outliers", "start": 61, "end": 80}}, {"head": {"text": "way", "start": 45, "end": 48}, "tail": {"text": "outlier detection", "start": 197, "end": 214}}, {"head": {"text": "way", "start": 45, "end": 48}, "tail": {"text": "interestingness prediction tasks", "start": 219, "end": 251}}, {"head": {"text": "interestingness prediction task", "start": 100, "end": 131}, "tail": {"text": "annotation outliers", "start": 61, "end": 80}}, {"head": {"text": "unified robust learning", "start": 137, "end": 160}, "tail": {"text": "interestingness prediction task", "start": 100, "end": 131}}, {"head": {"text": "unified robust learning", "start": 137, "end": 160}, "tail": {"text": "rank problem", "start": 164, "end": 176}}], "conjunction": [{"head": {"text": "outlier detection", "start": 197, "end": 214}, "tail": {"text": "interestingness prediction tasks", "start": 219, "end": 251}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments on both image and video interestingness benchmark datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives .", "output": {"relations": {"evaluate for": [{"head": {"text": "image and video interestingness benchmark datasets", "start": 30, "end": 80}, "tail": {"text": "approach", "start": 106, "end": 114}}], "compare": [{"head": {"text": "approach", "start": 106, "end": 114}, "tail": {"text": "state-of-the-art alternatives", "start": 141, "end": 170}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Many description logics -LRB- DLs -RRB- combine knowledge representation on an abstract , logical level with an interface to `` concrete '' domains such as numbers and strings .", "output": {"relations": {"conjunction": [{"head": {"text": "knowledge representation", "start": 48, "end": 72}, "tail": {"text": "description logics -LRB- DLs -RRB-", "start": 5, "end": 39}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe an implementation of data-driven selection of emphatic facial displays for an embodied conversational agent in a dialogue system .", "output": {"relations": {"used for": [{"head": {"text": "data-driven selection", "start": 33, "end": 54}, "tail": {"text": "embodied conversational agent", "start": 90, "end": 119}}, {"head": {"text": "emphatic facial displays", "start": 58, "end": 82}, "tail": {"text": "data-driven selection", "start": 33, "end": 54}}], "part of": [{"head": {"text": "embodied conversational agent", "start": 90, "end": 119}, "tail": {"text": "dialogue system", "start": 125, "end": 140}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The data from those recordings was used in a range of models for generating facial displays , each model making use of a different amount of context or choosing displays differently within a context .", "output": {"relations": {"used for": [{"head": {"text": "data", "start": 4, "end": 8}, "tail": {"text": "models", "start": 54, "end": 60}}, {"head": {"text": "models", "start": 54, "end": 60}, "tail": {"text": "facial displays", "start": 76, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The models were evaluated in two ways : by cross-validation against the corpus , and by asking users to rate the output .", "output": {"relations": {"evaluate for": [{"head": {"text": "cross-validation", "start": 43, "end": 59}, "tail": {"text": "models", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "When classifying high-dimensional sequence data , traditional methods -LRB- e.g. , HMMs , CRFs -RRB- may require large amounts of training data to avoid overfitting .", "output": {"relations": {"used for": [{"head": {"text": "HMMs", "start": 83, "end": 87}, "tail": {"text": "classifying high-dimensional sequence data", "start": 5, "end": 47}}, {"head": {"text": "CRFs", "start": 90, "end": 94}, "tail": {"text": "classifying high-dimensional sequence data", "start": 5, "end": 47}}], "conjunction": [{"head": {"text": "HMMs", "start": 83, "end": 87}, "tail": {"text": "CRFs", "start": 90, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently .", "output": {"relations": {"used for": [{"head": {"text": "dimensionality reduction", "start": 14, "end": 38}, "tail": {"text": "low-dimensional representation", "start": 65, "end": 95}}, {"head": {"text": "low-dimensional representation", "start": 65, "end": 95}, "tail": {"text": "classification", "start": 105, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Existing methods for supervised dimensionality reduction often presume that the data is densely sampled so that a neighborhood graph structure can be formed , or that the data arises from a known distribution .", "output": {"relations": {"used for": [{"head": {"text": "Existing methods", "start": 0, "end": 16}, "tail": {"text": "supervised dimensionality reduction", "start": 21, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Sufficient dimension reduction techniques aim to find a low dimensional representation such that the remaining degrees of freedom become conditionally independent of the output values .", "output": {"relations": {"used for": [{"head": {"text": "Sufficient dimension reduction techniques", "start": 0, "end": 41}, "tail": {"text": "low dimensional representation", "start": 56, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Spatial , temporal and periodic information is combined in a principled manner , and an optimal manifold is learned for the end-task .", "output": {"relations": {"used for": [{"head": {"text": "manifold", "start": 96, "end": 104}, "tail": {"text": "end-task", "start": 124, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate the effectiveness of our approach on several tasks involving the discrimination of human gesture and motion categories , as well as on a database of dynamic textures .", "output": {"relations": {"evaluate for": [{"head": {"text": "discrimination of human gesture and motion categories", "start": 80, "end": 133}, "tail": {"text": "approach", "start": 40, "end": 48}}, {"head": {"text": "database of dynamic textures", "start": 152, "end": 180}, "tail": {"text": "approach", "start": 40, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 24, "end": 33}, "tail": {"text": "chart-based phrase structure parsing", "start": 38, "end": 74}}, {"head": {"text": "natural language", "start": 78, "end": 94}, "tail": {"text": "chart-based phrase structure parsing", "start": 38, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This is facilitated through the use of phrase boundary heuristics based on the placement of function words , and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words .", "output": {"relations": {"used for": [{"head": {"text": "function words", "start": 92, "end": 106}, "tail": {"text": "phrase boundary heuristics", "start": 39, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges , thereby reducing the amount of ambiguity and thus the number of edges , since only edges with a valid semantic interpretation are ever introduced .", "output": {"relations": {"used for": [{"head": {"text": "semantic", "start": 61, "end": 69}, "tail": {"text": "reduction in the search space", "start": 10, "end": 39}}], "compare": [{"head": {"text": "semantic", "start": 61, "end": 69}, "tail": {"text": "syntactic categories", "start": 82, "end": 102}}], "feature of": [{"head": {"text": "semantic", "start": 61, "end": 69}, "tail": {"text": "terminal and non-terminal edges", "start": 110, "end": 141}}, {"head": {"text": "syntactic categories", "start": 82, "end": 102}, "tail": {"text": "terminal and non-terminal edges", "start": 110, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Automatic estimation of word significance oriented for speech-based Information Retrieval -LRB- IR -RRB- is addressed .", "output": {"relations": {"used for": [{"head": {"text": "Automatic estimation of word significance", "start": 0, "end": 41}, "tail": {"text": "speech-based Information Retrieval -LRB- IR -RRB-", "start": 55, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since the significance of words differs in IR , automatic speech recognition -LRB- ASR -RRB- performance has been evaluated based on weighted word error rate -LRB- WWER -RRB- , which gives a weight on errors from the viewpoint of IR , instead of word error rate -LRB- WER -RRB- , which treats all words uniformly .", "output": {"relations": {"evaluate for": [{"head": {"text": "weighted word error rate -LRB- WWER -RRB-", "start": 133, "end": 174}, "tail": {"text": "automatic speech recognition -LRB- ASR -RRB-", "start": 48, "end": 92}}], "compare": [{"head": {"text": "word error rate -LRB- WER -RRB-", "start": 246, "end": 277}, "tail": {"text": "weighted word error rate -LRB- WWER -RRB-", "start": 133, "end": 174}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A decoding strategy that minimizes WWER based on a Minimum Bayes-Risk framework has been shown , and the reduction of errors on both ASR and IR has been reported .", "output": {"relations": {"used for": [{"head": {"text": "decoding strategy", "start": 2, "end": 19}, "tail": {"text": "WWER", "start": 35, "end": 39}}, {"head": {"text": "Minimum Bayes-Risk framework", "start": 51, "end": 79}, "tail": {"text": "decoding strategy", "start": 2, "end": 19}}], "conjunction": [{"head": {"text": "ASR", "start": 133, "end": 136}, "tail": {"text": "IR", "start": 141, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose an automatic estimation method for word significance -LRB- weights -RRB- based on its influence on IR .", "output": {"relations": {"evaluate for": [{"head": {"text": "automatic estimation method", "start": 30, "end": 57}, "tail": {"text": "word significance -LRB- weights -RRB-", "start": 62, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , weights are estimated so that evaluation measures of ASR and IR are equivalent .", "output": {"relations": {"evaluate for": [{"head": {"text": "evaluation measures", "start": 45, "end": 64}, "tail": {"text": "ASR", "start": 68, "end": 71}}, {"head": {"text": "evaluation measures", "start": 45, "end": 64}, "tail": {"text": "IR", "start": 76, "end": 78}}], "conjunction": [{"head": {"text": "ASR", "start": 68, "end": 71}, "tail": {"text": "IR", "start": 76, "end": 78}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We apply the proposed method to a speech-based information retrieval system , which is a typical IR system , and show that the method works well .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "speech-based information retrieval system", "start": 34, "end": 75}}], "hyponym of": [{"head": {"text": "speech-based information retrieval system", "start": 34, "end": 75}, "tail": {"text": "IR system", "start": 97, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Methods developed for spelling correction for languages like English -LRB- see the review by Kukich -LRB- Kukich , 1992 -RRB- -RRB- are not readily applicable to agglutinative languages .", "output": {"relations": {"used for": [{"head": {"text": "Methods", "start": 0, "end": 7}, "tail": {"text": "spelling correction", "start": 22, "end": 41}}, {"head": {"text": "spelling correction", "start": 22, "end": 41}, "tail": {"text": "languages", "start": 46, "end": 55}}], "hyponym of": [{"head": {"text": "English", "start": 61, "end": 68}, "tail": {"text": "languages", "start": 46, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This poster presents an approach to spelling correction in agglutinative languages that is based on two-level morphology and a dynamic-programming based search algorithm .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 24, "end": 32}, "tail": {"text": "spelling correction", "start": 36, "end": 55}}, {"head": {"text": "agglutinative languages", "start": 59, "end": 82}, "tail": {"text": "spelling correction", "start": 36, "end": 55}}, {"head": {"text": "two-level morphology", "start": 100, "end": 120}, "tail": {"text": "spelling correction", "start": 36, "end": 55}}, {"head": {"text": "dynamic-programming based search algorithm", "start": 127, "end": 169}, "tail": {"text": "spelling correction", "start": 36, "end": 55}}], "conjunction": [{"head": {"text": "two-level morphology", "start": 100, "end": 120}, "tail": {"text": "dynamic-programming based search algorithm", "start": 127, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After an overview of our approach , we present results from experiments with spelling correction in Turkish .", "output": {"relations": {"used for": [{"head": {"text": "Turkish", "start": 100, "end": 107}, "tail": {"text": "spelling correction", "start": 77, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a novel training method for a localized phrase-based prediction model for statistical machine translation -LRB- SMT -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "training method", "start": 35, "end": 50}, "tail": {"text": "localized phrase-based prediction model", "start": 57, "end": 96}}, {"head": {"text": "localized phrase-based prediction model", "start": 57, "end": 96}, "tail": {"text": "statistical machine translation -LRB- SMT -RRB-", "start": 101, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model predicts blocks with orientation to handle local phrase re-ordering .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "local phrase re-ordering", "start": 53, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features -LRB- e.g. a language model score -RRB- as well as binary features based on the block identities themselves , e.g. block bigram features .", "output": {"relations": {"used for": [{"head": {"text": "maximum likelihood criterion", "start": 9, "end": 37}, "tail": {"text": "log-linear block bigram model", "start": 49, "end": 78}}, {"head": {"text": "real-valued features", "start": 90, "end": 110}, "tail": {"text": "log-linear block bigram model", "start": 49, "end": 78}}, {"head": {"text": "binary features", "start": 162, "end": 177}, "tail": {"text": "log-linear block bigram model", "start": 49, "end": 78}}], "conjunction": [{"head": {"text": "real-valued features", "start": 90, "end": 110}, "tail": {"text": "binary features", "start": 162, "end": 177}}], "hyponym of": [{"head": {"text": "language model score", "start": 124, "end": 144}, "tail": {"text": "real-valued features", "start": 90, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our training algorithm can easily handle millions of features .", "output": {"relations": {"used for": [{"head": {"text": "features", "start": 53, "end": 61}, "tail": {"text": "training algorithm", "start": 4, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The best system obtains a 18.6 % improvement over the baseline on a standard Arabic-English translation task .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 9, "end": 15}, "tail": {"text": "baseline", "start": 54, "end": 62}}], "evaluate for": [{"head": {"text": "Arabic-English translation task", "start": 77, "end": 108}, "tail": {"text": "system", "start": 9, "end": 15}}, {"head": {"text": "Arabic-English translation task", "start": 77, "end": 108}, "tail": {"text": "baseline", "start": 54, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations .", "output": {"relations": {"used for": [{"head": {"text": "data structure", "start": 34, "end": 48}, "tail": {"text": "phrase-based statistical machine translation", "start": 53, "end": 97}}, {"head": {"text": "data structure", "start": 34, "end": 48}, "tail": {"text": "retrieval of arbitrarily long phrases", "start": 119, "end": 156}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure .", "output": {"relations": {"conjunction": [{"head": {"text": "computational complexity", "start": 14, "end": 38}, "tail": {"text": "average retrieval times", "start": 43, "end": 66}}], "part of": [{"head": {"text": "phrase translations", "start": 82, "end": 101}, "tail": {"text": "suffix array-based data structure", "start": 109, "end": 142}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality .", "output": {"relations": {"evaluate for": [{"head": {"text": "retrieval time", "start": 47, "end": 61}, "tail": {"text": "sampling", "start": 12, "end": 20}}, {"head": {"text": "translation quality", "start": 101, "end": 120}, "tail": {"text": "sampling", "start": 12, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The major objective of this program is to develop and demonstrate robust , high performance continuous speech recognition -LRB- CSR -RRB- techniques focussed on application in Spoken Language Systems -LRB- SLS -RRB- which will enhance the effectiveness of military and civilian computer-based systems .", "output": {"relations": {"used for": [{"head": {"text": "continuous speech recognition -LRB- CSR -RRB- techniques", "start": 92, "end": 148}, "tail": {"text": "Spoken Language Systems -LRB- SLS -RRB-", "start": 176, "end": 215}}, {"head": {"text": "Spoken Language Systems -LRB- SLS -RRB-", "start": 176, "end": 215}, "tail": {"text": "military and civilian computer-based systems", "start": 256, "end": 300}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A key complementary objective is to define and develop applications of robust speech recognition and understanding systems , and to help catalyze the transition of spoken language technology into military and civilian systems , with particular focus on application of robust CSR to mobile military command and control .", "output": {"relations": {"used for": [{"head": {"text": "spoken language technology", "start": 164, "end": 190}, "tail": {"text": "military and civilian systems", "start": 196, "end": 225}}, {"head": {"text": "CSR", "start": 275, "end": 278}, "tail": {"text": "mobile military command and control", "start": 282, "end": 317}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The research effort focusses on developing advanced acoustic modelling , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .", "output": {"relations": {"conjunction": [{"head": {"text": "acoustic modelling", "start": 52, "end": 70}, "tail": {"text": "rapid search", "start": 73, "end": 85}}, {"head": {"text": "rapid search", "start": 73, "end": 85}, "tail": {"text": "recognition-time adaptation techniques", "start": 92, "end": 130}}], "used for": [{"head": {"text": "acoustic modelling", "start": 52, "end": 70}, "tail": {"text": "large-vocabulary CSR", "start": 142, "end": 162}}, {"head": {"text": "acoustic modelling", "start": 52, "end": 70}, "tail": {"text": "military application tasks", "start": 250, "end": 276}}, {"head": {"text": "rapid search", "start": 73, "end": 85}, "tail": {"text": "large-vocabulary CSR", "start": 142, "end": 162}}, {"head": {"text": "rapid search", "start": 73, "end": 85}, "tail": {"text": "military application tasks", "start": 250, "end": 276}}, {"head": {"text": "recognition-time adaptation techniques", "start": 92, "end": 130}, "tail": {"text": "large-vocabulary CSR", "start": 142, "end": 162}}, {"head": {"text": "recognition-time adaptation techniques", "start": 92, "end": 130}, "tail": {"text": "military application tasks", "start": 250, "end": 276}}, {"head": {"text": "large-vocabulary CSR", "start": 142, "end": 162}, "tail": {"text": "ARPA large-vocabulary CSR corpora", "start": 209, "end": 242}}, {"head": {"text": "techniques", "start": 120, "end": 130}, "tail": {"text": "military application tasks", "start": 250, "end": 276}}], "hyponym of": [{"head": {"text": "acoustic modelling", "start": 52, "end": 70}, "tail": {"text": "techniques", "start": 120, "end": 130}}, {"head": {"text": "rapid search", "start": 73, "end": 85}, "tail": {"text": "techniques", "start": 120, "end": 130}}, {"head": {"text": "recognition-time adaptation techniques", "start": 92, "end": 130}, "tail": {"text": "techniques", "start": 120, "end": 130}}], "evaluate for": [{"head": {"text": "ARPA large-vocabulary CSR corpora", "start": 209, "end": 242}, "tail": {"text": "techniques", "start": 120, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model .", "output": {"relations": {"used for": [{"head": {"text": "word vectors", "start": 93, "end": 105}, "tail": {"text": "similarity between words", "start": 33, "end": 57}}, {"head": {"text": "vector space model", "start": 113, "end": 131}, "tail": {"text": "word vectors", "start": 93, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Through two experiments , three methods for constructing word vectors , i.e. , LSA-based , cooccurrence-based and dictionary-based methods , were compared in terms of the ability to represent two kinds of similarity , i.e. , taxonomic similarity and associative similarity .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 32, "end": 39}, "tail": {"text": "constructing word vectors", "start": 44, "end": 69}}, {"head": {"text": "LSA-based , cooccurrence-based and dictionary-based methods", "start": 79, "end": 138}, "tail": {"text": "similarity", "start": 205, "end": 215}}], "hyponym of": [{"head": {"text": "taxonomic similarity", "start": 225, "end": 245}, "tail": {"text": "similarity", "start": 205, "end": 215}}, {"head": {"text": "associative similarity", "start": 250, "end": 272}, "tail": {"text": "similarity", "start": 205, "end": 215}}], "conjunction": [{"head": {"text": "taxonomic similarity", "start": 225, "end": 245}, "tail": {"text": "associative similarity", "start": 250, "end": 272}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity , while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity .", "output": {"relations": {"used for": [{"head": {"text": "dictionary-based word vectors", "start": 42, "end": 71}, "tail": {"text": "taxonomic similarity", "start": 87, "end": 107}}, {"head": {"text": "LSA-based and the cooccurrence-based word vectors", "start": 120, "end": 169}, "tail": {"text": "associative similarity", "start": 185, "end": 207}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data .", "output": {"relations": {"used for": [{"head": {"text": "Arabic-English", "start": 67, "end": 81}, "tail": {"text": "maximum entropy word alignment algorithm", "start": 22, "end": 62}}, {"head": {"text": "supervised training data", "start": 91, "end": 115}, "tail": {"text": "maximum entropy word alignment algorithm", "start": 22, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance .", "output": {"relations": {"used for": [{"head": {"text": "training material", "start": 45, "end": 62}, "tail": {"text": "machine translation", "start": 79, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The probabilistic model used in the alignment directly models the link decisions .", "output": {"relations": {"used for": [{"head": {"text": "probabilistic model", "start": 4, "end": 23}, "tail": {"text": "alignment", "start": 36, "end": 45}}, {"head": {"text": "probabilistic model", "start": 4, "end": 23}, "tail": {"text": "link decisions", "start": 66, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests .", "output": {"relations": {"used for": [{"head": {"text": "word alignment techniques", "start": 41, "end": 66}, "tail": {"text": "machine translation tests", "start": 110, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Performance of the algorithm is contrasted with human annotation performance .", "output": {"relations": {"compare": [{"head": {"text": "algorithm", "start": 19, "end": 28}, "tail": {"text": "human annotation", "start": 48, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a novel Cooperative Model for natural language understanding in a dialogue system .", "output": {"relations": {"used for": [{"head": {"text": "Cooperative Model", "start": 35, "end": 52}, "tail": {"text": "natural language understanding", "start": 57, "end": 87}}, {"head": {"text": "natural language understanding", "start": 57, "end": 87}, "tail": {"text": "dialogue system", "start": 93, "end": 108}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We build this based on both Finite State Model -LRB- FSM -RRB- and Statistical Learning Model -LRB- SLM -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "Finite State Model -LRB- FSM -RRB-", "start": 28, "end": 62}, "tail": {"text": "this", "start": 9, "end": 13}}, {"head": {"text": "Statistical Learning Model -LRB- SLM -RRB-", "start": 67, "end": 109}, "tail": {"text": "this", "start": 9, "end": 13}}], "conjunction": [{"head": {"text": "Finite State Model -LRB- FSM -RRB-", "start": 28, "end": 62}, "tail": {"text": "Statistical Learning Model -LRB- SLM -RRB-", "start": 67, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility .", "output": {"relations": {"used for": [{"head": {"text": "FSM", "start": 0, "end": 3}, "tail": {"text": "language understanding", "start": 32, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The ambiguity resolution of right-side dependencies is essential for dependency parsing of sentences with two or more verbs .", "output": {"relations": {"used for": [{"head": {"text": "ambiguity resolution of right-side dependencies", "start": 4, "end": 51}, "tail": {"text": "dependency parsing", "start": 69, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous works on shift-reduce dependency parsers may not guarantee the connectivity of a dependency tree due to their weakness at resolving the right-side dependencies .", "output": {"relations": {"evaluate for": [{"head": {"text": "connectivity", "start": 72, "end": 84}, "tail": {"text": "dependency tree", "start": 90, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a two-phase shift-reduce dependency parser based on SVM learning .", "output": {"relations": {"used for": [{"head": {"text": "SVM learning", "start": 72, "end": 84}, "tail": {"text": "two-phase shift-reduce dependency parser", "start": 22, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The left-side dependents and right-side nominal dependents are detected in Phase I , and right-side verbal dependents are decided in Phase II .", "output": {"relations": {"conjunction": [{"head": {"text": "left-side dependents", "start": 4, "end": 24}, "tail": {"text": "right-side nominal dependents", "start": 29, "end": 58}}, {"head": {"text": "right-side verbal dependents", "start": 89, "end": 117}, "tail": {"text": "right-side nominal dependents", "start": 29, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In experimental evaluation , our proposed method outperforms previous shift-reduce dependency parsers for the Chine language , showing improvement of dependency accuracy by 10.08 % .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 42, "end": 48}, "tail": {"text": "shift-reduce dependency parsers", "start": 70, "end": 101}}], "evaluate for": [{"head": {"text": "Chine language", "start": 110, "end": 124}, "tail": {"text": "method", "start": 42, "end": 48}}, {"head": {"text": "Chine language", "start": 110, "end": 124}, "tail": {"text": "shift-reduce dependency parsers", "start": 70, "end": 101}}, {"head": {"text": "dependency accuracy", "start": 150, "end": 169}, "tail": {"text": "method", "start": 42, "end": 48}}, {"head": {"text": "dependency accuracy", "start": 150, "end": 169}, "tail": {"text": "shift-reduce dependency parsers", "start": 70, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By using commands or rules which are defined to facilitate the construction of format expected or some mathematical expressions , elaborate and pretty documents can be successfully obtained .", "output": {"relations": {"conjunction": [{"head": {"text": "commands", "start": 9, "end": 17}, "tail": {"text": "rules", "start": 21, "end": 26}}], "used for": [{"head": {"text": "commands", "start": 9, "end": 17}, "tail": {"text": "mathematical expressions", "start": 103, "end": 127}}, {"head": {"text": "rules", "start": 21, "end": 26}, "tail": {"text": "mathematical expressions", "start": 103, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an evaluation method employing a latent variable model for paraphrases with their contexts .", "output": {"relations": {"evaluate for": [{"head": {"text": "evaluation method", "start": 23, "end": 40}, "tail": {"text": "paraphrases", "start": 79, "end": 90}}], "used for": [{"head": {"text": "latent variable model", "start": 53, "end": 74}, "tail": {"text": "evaluation method", "start": 23, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results also revealed an upper bound of accuracy of 77 % with the method when using only topic information .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 44, "end": 52}, "tail": {"text": "method", "start": 70, "end": 76}}], "used for": [{"head": {"text": "topic information", "start": 93, "end": 110}, "tail": {"text": "method", "start": 70, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe the methods and hardware that we are using to produce a real-time demonstration of an integrated Spoken Language System .", "output": {"relations": {"conjunction": [{"head": {"text": "methods", "start": 16, "end": 23}, "tail": {"text": "hardware", "start": 28, "end": 36}}], "used for": [{"head": {"text": "methods", "start": 16, "end": 23}, "tail": {"text": "integrated Spoken Language System", "start": 98, "end": 131}}, {"head": {"text": "hardware", "start": 28, "end": 36}, "tail": {"text": "integrated Spoken Language System", "start": 98, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe algorithms that greatly reduce the computation needed to compute the N-Best sentence hypotheses .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 12, "end": 22}, "tail": {"text": "N-Best sentence hypotheses", "start": 81, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To avoid grammar coverage problems we use a fully-connected first-order statistical class grammar .", "output": {"relations": {"used for": [{"head": {"text": "fully-connected first-order statistical class grammar", "start": 44, "end": 97}, "tail": {"text": "grammar coverage problems", "start": 9, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The speech-search algorithm is implemented on a board with a single Intel i860 chip , which provides a factor of 5 speed-up over a SUN 4 for straight C code .", "output": {"relations": {"used for": [{"head": {"text": "board", "start": 48, "end": 53}, "tail": {"text": "speech-search algorithm", "start": 4, "end": 27}}, {"head": {"text": "Intel i860 chip", "start": 68, "end": 83}, "tail": {"text": "straight C code", "start": 141, "end": 156}}, {"head": {"text": "SUN 4", "start": 131, "end": 136}, "tail": {"text": "straight C code", "start": 141, "end": 156}}], "part of": [{"head": {"text": "Intel i860 chip", "start": 68, "end": 83}, "tail": {"text": "board", "start": 48, "end": 53}}], "compare": [{"head": {"text": "Intel i860 chip", "start": 68, "end": 83}, "tail": {"text": "SUN 4", "start": 131, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The board plugs directly into the VME bus of the SUN4 , which controls the system and contains the natural language system and application back end .", "output": {"relations": {"used for": [{"head": {"text": "board", "start": 4, "end": 9}, "tail": {"text": "system", "start": 75, "end": 81}}], "part of": [{"head": {"text": "VME bus", "start": 34, "end": 41}, "tail": {"text": "SUN4", "start": 49, "end": 53}}, {"head": {"text": "natural language system", "start": 99, "end": 122}, "tail": {"text": "board", "start": 4, "end": 9}}, {"head": {"text": "application back end", "start": 127, "end": 147}, "tail": {"text": "board", "start": 4, "end": 9}}], "conjunction": [{"head": {"text": "natural language system", "start": 99, "end": 122}, "tail": {"text": "application back end", "start": 127, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We address the problem of estimating location information of an image using principles from automated representation learning .", "output": {"relations": {"used for": [{"head": {"text": "image", "start": 64, "end": 69}, "tail": {"text": "estimating location information", "start": 26, "end": 57}}, {"head": {"text": "automated representation learning", "start": 92, "end": 125}, "tail": {"text": "estimating location information", "start": 26, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations , by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds .", "output": {"relations": {"used for": [{"head": {"text": "geometric prior", "start": 146, "end": 161}, "tail": {"text": "it", "start": 125, "end": 127}}, {"head": {"text": "parallel transport on manifolds", "start": 294, "end": 325}, "tail": {"text": "geometric prior", "start": 146, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations , and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 20, "end": 28}, "tail": {"text": "heterogeneous data modalities", "start": 64, "end": 93}}, {"head": {"text": "transferring knowledge", "start": 213, "end": 235}, "tail": {"text": "grouping of data", "start": 282, "end": 298}}], "hyponym of": [{"head": {"text": "geo-tags", "start": 102, "end": 110}, "tail": {"text": "heterogeneous data modalities", "start": 64, "end": 93}}, {"head": {"text": "videos", "start": 115, "end": 121}, "tail": {"text": "heterogeneous data modalities", "start": 64, "end": 93}}], "conjunction": [{"head": {"text": "geo-tags", "start": 102, "end": 110}, "tail": {"text": "videos", "start": 115, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate our approach on several standard datasets such as im2gps , San Francisco and MediaEval2010 , and obtain state-of-the-art results .", "output": {"relations": {"evaluate for": [{"head": {"text": "datasets", "start": 45, "end": 53}, "tail": {"text": "approach", "start": 16, "end": 24}}], "hyponym of": [{"head": {"text": "im2gps", "start": 62, "end": 68}, "tail": {"text": "datasets", "start": 45, "end": 53}}, {"head": {"text": "San Francisco", "start": 71, "end": 84}, "tail": {"text": "datasets", "start": 45, "end": 53}}, {"head": {"text": "MediaEval2010", "start": 89, "end": 102}, "tail": {"text": "datasets", "start": 45, "end": 53}}], "conjunction": [{"head": {"text": "im2gps", "start": 62, "end": 68}, "tail": {"text": "San Francisco", "start": 71, "end": 84}}, {"head": {"text": "San Francisco", "start": 71, "end": 84}, "tail": {"text": "MediaEval2010", "start": 89, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Conventional HMMs have weak duration constraints .", "output": {"relations": {"feature of": [{"head": {"text": "weak duration constraints", "start": 23, "end": 48}, "tail": {"text": "HMMs", "start": 13, "end": 17}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In noisy conditions , the mismatch between corrupted speech signals and models trained on clean speech may cause the decoder to produce word matches with unrealistic durations .", "output": {"relations": {"used for": [{"head": {"text": "clean speech", "start": 90, "end": 102}, "tail": {"text": "models", "start": 72, "end": 78}}, {"head": {"text": "decoder", "start": 117, "end": 124}, "tail": {"text": "word matches", "start": 136, "end": 148}}], "feature of": [{"head": {"text": "unrealistic durations", "start": 154, "end": 175}, "tail": {"text": "word matches", "start": 136, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a simple way to incorporate word duration constraints by unrolling HMMs to form a lattice where word duration probabilities can be applied directly to state transitions .", "output": {"relations": {"used for": [{"head": {"text": "unrolling HMMs", "start": 77, "end": 91}, "tail": {"text": "word duration constraints", "start": 48, "end": 73}}, {"head": {"text": "unrolling HMMs", "start": 77, "end": 91}, "tail": {"text": "lattice", "start": 102, "end": 109}}, {"head": {"text": "word duration probabilities", "start": 116, "end": 143}, "tail": {"text": "state transitions", "start": 171, "end": 188}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The expanded HMMs are compatible with conventional Viterbi decoding .", "output": {"relations": {"conjunction": [{"head": {"text": "Viterbi decoding", "start": 51, "end": 67}, "tail": {"text": "HMMs", "start": 13, "end": 17}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments on connected-digit recognition show that when using explicit duration constraints the decoder generates word matches with more reasonable durations , and word error rates are significantly reduced across a broad range of noise conditions .", "output": {"relations": {"used for": [{"head": {"text": "connected-digit recognition", "start": 15, "end": 42}, "tail": {"text": "decoder", "start": 98, "end": 105}}, {"head": {"text": "duration constraints", "start": 73, "end": 93}, "tail": {"text": "decoder", "start": 98, "end": 105}}, {"head": {"text": "decoder", "start": 98, "end": 105}, "tail": {"text": "word matches", "start": 116, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One of the claimed benefits of Tree Adjoining Grammars is that they have an extended domain of locality -LRB- EDOL -RRB- .", "output": {"relations": {"feature of": [{"head": {"text": "Tree Adjoining Grammars", "start": 31, "end": 54}, "tail": {"text": "extended domain of locality -LRB- EDOL -RRB-", "start": 76, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We consider how this can be exploited to limit the need for feature structure unification during parsing .", "output": {"relations": {"used for": [{"head": {"text": "feature structure unification", "start": 60, "end": 89}, "tail": {"text": "parsing", "start": 97, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compare two wide-coverage lexicalized grammars of English , LEXSYS and XTAG , finding that the two grammars exploit EDOL in different ways .", "output": {"relations": {"hyponym of": [{"head": {"text": "LEXSYS", "start": 63, "end": 69}, "tail": {"text": "lexicalized grammars of English", "start": 29, "end": 60}}, {"head": {"text": "XTAG", "start": 74, "end": 78}, "tail": {"text": "lexicalized grammars of English", "start": 29, "end": 60}}], "compare": [{"head": {"text": "LEXSYS", "start": 63, "end": 69}, "tail": {"text": "XTAG", "start": 74, "end": 78}}], "used for": [{"head": {"text": "grammars", "start": 41, "end": 49}, "tail": {"text": "EDOL", "start": 119, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Identity uncertainty is a pervasive problem in real-world data analysis .", "output": {"relations": {"hyponym of": [{"head": {"text": "Identity uncertainty", "start": 0, "end": 20}, "tail": {"text": "real-world data analysis", "start": 47, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our approach is based on the use of a relational probability model to define a generative model for the domain , including models of author and title corruption and a probabilistic citation grammar .", "output": {"relations": {"used for": [{"head": {"text": "relational probability model", "start": 38, "end": 66}, "tail": {"text": "approach", "start": 4, "end": 12}}, {"head": {"text": "relational probability model", "start": 38, "end": 66}, "tail": {"text": "generative model", "start": 79, "end": 95}}, {"head": {"text": "generative model", "start": 79, "end": 95}, "tail": {"text": "domain", "start": 104, "end": 110}}], "part of": [{"head": {"text": "models of author and title corruption", "start": 123, "end": 160}, "tail": {"text": "relational probability model", "start": 38, "end": 66}}, {"head": {"text": "probabilistic citation grammar", "start": 167, "end": 197}, "tail": {"text": "relational probability model", "start": 38, "end": 66}}], "conjunction": [{"head": {"text": "models of author and title corruption", "start": 123, "end": 160}, "tail": {"text": "probabilistic citation grammar", "start": 167, "end": 197}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Identity uncertainty is handled by extending standard models to incorporate probabilities over the possible mappings between terms in the language and objects in the domain .", "output": {"relations": {"used for": [{"head": {"text": "models", "start": 54, "end": 60}, "tail": {"text": "Identity uncertainty", "start": 0, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Inference is based on Markov chain Monte Carlo , augmented with specific methods for generating efficient proposals when the domain contains many objects .", "output": {"relations": {"used for": [{"head": {"text": "Markov chain Monte Carlo", "start": 22, "end": 46}, "tail": {"text": "Inference", "start": 0, "end": 9}}, {"head": {"text": "methods", "start": 73, "end": 80}, "tail": {"text": "Inference", "start": 0, "end": 9}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Results on several citation data sets show that the method outperforms current algorithms for citation matching .", "output": {"relations": {"evaluate for": [{"head": {"text": "citation data sets", "start": 19, "end": 37}, "tail": {"text": "method", "start": 52, "end": 58}}], "compare": [{"head": {"text": "method", "start": 52, "end": 58}, "tail": {"text": "current algorithms", "start": 71, "end": 89}}], "used for": [{"head": {"text": "method", "start": 52, "end": 58}, "tail": {"text": "citation matching", "start": 94, "end": 111}}, {"head": {"text": "current algorithms", "start": 71, "end": 89}, "tail": {"text": "citation matching", "start": 94, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The declarative , relational nature of the model also means that our algorithm can determine object characteristics such as author names by combining multiple citations of multiple papers .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 69, "end": 78}, "tail": {"text": "object characteristics", "start": 93, "end": 115}}], "hyponym of": [{"head": {"text": "author names", "start": 124, "end": 136}, "tail": {"text": "object characteristics", "start": 93, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization .", "output": {"relations": {"used for": [{"head": {"text": "supervised learning", "start": 63, "end": 82}, "tail": {"text": "human biases in summarization", "start": 123, "end": 152}}, {"head": {"text": "unsupervised learning", "start": 88, "end": 109}, "tail": {"text": "human biases in summarization", "start": 123, "end": 152}}], "conjunction": [{"head": {"text": "unsupervised learning", "start": 88, "end": 109}, "tail": {"text": "supervised learning", "start": 63, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries .", "output": {"relations": {"feature of": [{"head": {"text": "clustering framework", "start": 77, "end": 97}, "tail": {"text": "probabilistic decision tree", "start": 38, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The corpus of human created extracts is created from a newspaper corpus and used as a test set .", "output": {"relations": {"used for": [{"head": {"text": "newspaper corpus", "start": 55, "end": 71}, "tail": {"text": "corpus of human created extracts", "start": 4, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We build probabilistic decision trees of different flavors and integrate each of them with the clustering framework .", "output": {"relations": {"conjunction": [{"head": {"text": "clustering framework", "start": 95, "end": 115}, "tail": {"text": "them", "start": 81, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this study , we propose a knowledge-independent method for aligning terms and thus extracting translations from a small , domain-specific corpus consisting of parallel English and Chinese court judgments from Hong Kong .", "output": {"relations": {"part of": [{"head": {"text": "parallel English and Chinese court judgments", "start": 162, "end": 206}, "tail": {"text": "small , domain-specific corpus", "start": 117, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With a sentence-aligned corpus , translation equivalences are suggested by analysing the frequency profiles of parallel concordances .", "output": {"relations": {"used for": [{"head": {"text": "sentence-aligned corpus", "start": 7, "end": 30}, "tail": {"text": "translation equivalences", "start": 33, "end": 57}}], "part of": [{"head": {"text": "frequency profiles", "start": 89, "end": 107}, "tail": {"text": "parallel concordances", "start": 111, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method overcomes the limitations of conventional statistical methods which require large corpora to be effective , and lexical approaches which depend on existing bilingual dictionaries .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "statistical methods", "start": 53, "end": 72}}, {"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "lexical approaches", "start": 123, "end": 141}}], "used for": [{"head": {"text": "large corpora", "start": 87, "end": 100}, "tail": {"text": "statistical methods", "start": 53, "end": 72}}, {"head": {"text": "bilingual dictionaries", "start": 167, "end": 189}, "tail": {"text": "lexical approaches", "start": 123, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Pilot testing on a parallel corpus of about 113K Chinese words and 120K English words gives an encouraging 85 % precision and 45 % recall .", "output": {"relations": {"conjunction": [{"head": {"text": "precision", "start": 112, "end": 121}, "tail": {"text": "recall", "start": 131, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Future work includes fine-tuning the algorithm upon the analysis of the errors , and acquiring a translation lexicon for legal terminology by filtering out general terms .", "output": {"relations": {"used for": [{"head": {"text": "translation lexicon", "start": 97, "end": 116}, "tail": {"text": "legal terminology", "start": 121, "end": 138}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Traditional machine learning techniques have been applied to this problem with reasonable success , but they have been shown to work well only when there is a good match between the training and test data with respect to topic .", "output": {"relations": {"used for": [{"head": {"text": "machine learning techniques", "start": 12, "end": 39}, "tail": {"text": "problem", "start": 66, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper demonstrates that match with respect to domain and time is also important , and presents preliminary experiments with training data labeled with emoticons , which has the potential of being independent of domain , topic and time .", "output": {"relations": {"feature of": [{"head": {"text": "emoticons", "start": 156, "end": 165}, "tail": {"text": "training data", "start": 129, "end": 142}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a novel algorithm for estimating the broad 3D geometric structure of outdoor video scenes .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 19, "end": 28}, "tail": {"text": "3D geometric structure of outdoor video scenes", "start": 54, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Leveraging spatio-temporal video segmentation , we decompose a dynamic scene captured by a video into geometric classes , based on predictions made by region-classifiers that are trained on appearance and motion features .", "output": {"relations": {"used for": [{"head": {"text": "spatio-temporal video segmentation", "start": 11, "end": 45}, "tail": {"text": "dynamic scene", "start": 63, "end": 76}}, {"head": {"text": "region-classifiers", "start": 151, "end": 169}, "tail": {"text": "geometric classes", "start": 102, "end": 119}}, {"head": {"text": "appearance and motion features", "start": 190, "end": 220}, "tail": {"text": "region-classifiers", "start": 151, "end": 169}}], "part of": [{"head": {"text": "geometric classes", "start": 102, "end": 119}, "tail": {"text": "dynamic scene", "start": 63, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We built a novel , extensive dataset on geometric context of video to evaluate our method , consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames .", "output": {"relations": {"evaluate for": [{"head": {"text": "dataset", "start": 29, "end": 36}, "tail": {"text": "method", "start": 83, "end": 89}}], "feature of": [{"head": {"text": "geometric context of video", "start": 40, "end": 66}, "tail": {"text": "dataset", "start": 29, "end": 36}}], "part of": [{"head": {"text": "annotated outdoor videos", "start": 128, "end": 152}, "tail": {"text": "dataset", "start": 29, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To further scale beyond this dataset , we propose a semi-supervised learning framework to expand the pool of labeled data with high confidence predictions obtained from unlabeled data .", "output": {"relations": {"used for": [{"head": {"text": "semi-supervised learning framework", "start": 52, "end": 86}, "tail": {"text": "labeled data", "start": 109, "end": 121}}, {"head": {"text": "high confidence predictions", "start": 127, "end": 154}, "tail": {"text": "semi-supervised learning framework", "start": 52, "end": 86}}, {"head": {"text": "unlabeled data", "start": 169, "end": 183}, "tail": {"text": "high confidence predictions", "start": 127, "end": 154}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our system produces an accurate prediction of geometric context of video achieving 96 % accuracy across main geometric classes .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "geometric context of video", "start": 46, "end": 72}}], "evaluate for": [{"head": {"text": "accuracy", "start": 88, "end": 96}, "tail": {"text": "system", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a system -LRB- RAREAS -RRB- which synthesizes marine weather forecasts directly from formatted weather data .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 23, "end": 29}, "tail": {"text": "marine weather forecasts", "start": 67, "end": 91}}, {"head": {"text": "formatted weather data", "start": 106, "end": 128}, "tail": {"text": "system", "start": 23, "end": 29}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such synthesis appears feasible in certain natural sublanguages with stereotyped text structure .", "output": {"relations": {"used for": [{"head": {"text": "natural sublanguages with stereotyped text structure", "start": 43, "end": 95}, "tail": {"text": "synthesis", "start": 5, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "RAREAS draws on several kinds of linguistic and non-linguistic knowledge and mirrors a forecaster 's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events .", "output": {"relations": {"used for": [{"head": {"text": "linguistic and non-linguistic knowledge", "start": 33, "end": 72}, "tail": {"text": "RAREAS", "start": 0, "end": 6}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The approach can easily be adapted to synthesize bilingual or multi-lingual texts .", "output": {"relations": {"used for": [{"head": {"text": "bilingual or multi-lingual texts", "start": 49, "end": 81}, "tail": {"text": "approach", "start": 4, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We go , on to describe FlexP , a bottom-up pattern-matching parser that we have designed and implemented to provide these flexibilities for restricted natural language input to a limited-domain computer system .", "output": {"relations": {"hyponym of": [{"head": {"text": "FlexP", "start": 23, "end": 28}, "tail": {"text": "bottom-up pattern-matching parser", "start": 33, "end": 66}}], "used for": [{"head": {"text": "bottom-up pattern-matching parser", "start": 33, "end": 66}, "tail": {"text": "flexibilities", "start": 122, "end": 135}}, {"head": {"text": "restricted natural language", "start": 140, "end": 167}, "tail": {"text": "bottom-up pattern-matching parser", "start": 33, "end": 66}}], "feature of": [{"head": {"text": "flexibilities", "start": 122, "end": 135}, "tail": {"text": "restricted natural language", "start": 140, "end": 167}}], "part of": [{"head": {"text": "flexibilities", "start": 122, "end": 135}, "tail": {"text": "limited-domain computer system", "start": 179, "end": 209}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Traditional information retrieval techniques use a histogram of keywords as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance .", "output": {"relations": {"used for": [{"head": {"text": "histogram of keywords", "start": 51, "end": 72}, "tail": {"text": "information retrieval techniques", "start": 12, "end": 44}}, {"head": {"text": "histogram of keywords", "start": 51, "end": 72}, "tail": {"text": "document representation", "start": 80, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An alternative index could be the activity such as discussing , planning , informing , story-telling , etc. .", "output": {"relations": {"hyponym of": [{"head": {"text": "discussing", "start": 51, "end": 61}, "tail": {"text": "activity", "start": 34, "end": 42}}, {"head": {"text": "planning", "start": 64, "end": 72}, "tail": {"text": "activity", "start": 34, "end": 42}}, {"head": {"text": "informing", "start": 75, "end": 84}, "tail": {"text": "activity", "start": 34, "end": 42}}, {"head": {"text": "story-telling", "start": 87, "end": 100}, "tail": {"text": "activity", "start": 34, "end": 42}}], "conjunction": [{"head": {"text": "discussing", "start": 51, "end": 61}, "tail": {"text": "planning", "start": 64, "end": 72}}, {"head": {"text": "planning", "start": 64, "end": 72}, "tail": {"text": "informing", "start": 75, "end": 84}}, {"head": {"text": "informing", "start": 75, "end": 84}, "tail": {"text": "story-telling", "start": 87, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper addresses the problem of the automatic detection of those activities in meeting situation and everyday rejoinders .", "output": {"relations": {"used for": [{"head": {"text": "activities", "start": 69, "end": 79}, "tail": {"text": "automatic detection", "start": 40, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The format of the corpus adopts the Child Language Data Exchange System -LRB- CHILDES -RRB- .", "output": {"relations": {"feature of": [{"head": {"text": "Child Language Data Exchange System -LRB- CHILDES -RRB-", "start": 36, "end": 91}, "tail": {"text": "corpus", "start": 18, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we describe data collection , transcription , word segmentation , and part-of-speech annotation of this corpus .", "output": {"relations": {"conjunction": [{"head": {"text": "data collection", "start": 28, "end": 43}, "tail": {"text": "transcription", "start": 46, "end": 59}}, {"head": {"text": "transcription", "start": 46, "end": 59}, "tail": {"text": "word segmentation", "start": 62, "end": 79}}, {"head": {"text": "word segmentation", "start": 62, "end": 79}, "tail": {"text": "part-of-speech annotation", "start": 86, "end": 111}}], "used for": [{"head": {"text": "data collection", "start": 28, "end": 43}, "tail": {"text": "corpus", "start": 120, "end": 126}}, {"head": {"text": "transcription", "start": 46, "end": 59}, "tail": {"text": "corpus", "start": 120, "end": 126}}, {"head": {"text": "word segmentation", "start": 62, "end": 79}, "tail": {"text": "corpus", "start": 120, "end": 126}}, {"head": {"text": "part-of-speech annotation", "start": 86, "end": 111}, "tail": {"text": "corpus", "start": 120, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper shows how dictionary word sense definitions can be analysed by applying a hierarchy of phrasal patterns .", "output": {"relations": {"used for": [{"head": {"text": "hierarchy of phrasal patterns", "start": 85, "end": 114}, "tail": {"text": "dictionary word sense definitions", "start": 21, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An experimental system embodying this mechanism has been implemented for processing definitions from the Longman Dictionary of Contemporary English .", "output": {"relations": {"part of": [{"head": {"text": "mechanism", "start": 38, "end": 47}, "tail": {"text": "system", "start": 16, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A property of this dictionary , exploited by the system , is that it uses a restricted vocabulary in its word sense definitions .", "output": {"relations": {"used for": [{"head": {"text": "restricted vocabulary", "start": 76, "end": 97}, "tail": {"text": "it", "start": 37, "end": 39}}, {"head": {"text": "restricted vocabulary", "start": 76, "end": 97}, "tail": {"text": "word sense definitions", "start": 105, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The structures generated by the experimental system are intended to be used for the classification of new word senses in terms of the senses of words in the restricted vocabulary .", "output": {"relations": {"used for": [{"head": {"text": "restricted vocabulary", "start": 157, "end": 178}, "tail": {"text": "classification of new word senses", "start": 84, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Thus the work reported addresses two robustness problems faced by current experimental natural language processing systems : coping with an incomplete lexicon and with incomplete knowledge of phrasal constructions .", "output": {"relations": {"feature of": [{"head": {"text": "robustness problems", "start": 37, "end": 56}, "tail": {"text": "natural language processing systems", "start": 87, "end": 122}}], "hyponym of": [{"head": {"text": "incomplete lexicon", "start": 140, "end": 158}, "tail": {"text": "robustness problems", "start": 37, "end": 56}}, {"head": {"text": "incomplete knowledge of phrasal constructions", "start": 168, "end": 213}, "tail": {"text": "robustness problems", "start": 37, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified approach to word breaking and OOV identification .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 100, "end": 108}, "tail": {"text": "word segmentation system", "start": 22, "end": 46}}, {"head": {"text": "approach", "start": 100, "end": 108}, "tail": {"text": "word breaking", "start": 112, "end": 125}}, {"head": {"text": "approach", "start": 100, "end": 108}, "tail": {"text": "OOV identification", "start": 130, "end": 148}}], "conjunction": [{"head": {"text": "word breaking", "start": 112, "end": 125}, "tail": {"text": "OOV identification", "start": 130, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .", "output": {"relations": {"hyponym of": [{"head": {"text": "PK-open", "start": 73, "end": 80}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "PK-closed", "start": 83, "end": 92}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "AS-open", "start": 95, "end": 102}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "AS-closed", "start": 105, "end": 114}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "HK-open", "start": 117, "end": 124}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "HK-closed", "start": 127, "end": 136}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "MSR-open", "start": 139, "end": 147}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}, {"head": {"text": "MSR - closed", "start": 152, "end": 164}, "tail": {"text": "segmentation bakeoff", "start": 49, "end": 69}}], "conjunction": [{"head": {"text": "PK-open", "start": 73, "end": 80}, "tail": {"text": "PK-closed", "start": 83, "end": 92}}, {"head": {"text": "PK-closed", "start": 83, "end": 92}, "tail": {"text": "AS-open", "start": 95, "end": 102}}, {"head": {"text": "AS-open", "start": 95, "end": 102}, "tail": {"text": "AS-closed", "start": 105, "end": 114}}, {"head": {"text": "AS-closed", "start": 105, "end": 114}, "tail": {"text": "HK-open", "start": 117, "end": 124}}, {"head": {"text": "HK-open", "start": 117, "end": 124}, "tail": {"text": "HK-closed", "start": 127, "end": 136}}, {"head": {"text": "HK-closed", "start": 127, "end": 136}, "tail": {"text": "MSR-open", "start": 139, "end": 147}}, {"head": {"text": "MSR-open", "start": 139, "end": 147}, "tail": {"text": "MSR - closed", "start": 152, "end": 164}}, {"head": {"text": "MSR-open", "start": 139, "end": 147}, "tail": {"text": "MSR-close", "start": 228, "end": 237}}, {"head": {"text": "MSR-close", "start": 228, "end": 237}, "tail": {"text": "PK-open", "start": 73, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a method of interactively visualizing and directing the process of translating a sentence .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 23, "end": 29}, "tail": {"text": "interactively visualizing and directing the process of translating", "start": 33, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method allows a user to explore a model of syntax-based statistical machine translation -LRB- MT -RRB- , to understand the model 's strengths and weaknesses , and to compare it to other MT systems .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "model", "start": 38, "end": 43}}, {"head": {"text": "model", "start": 38, "end": 43}, "tail": {"text": "syntax-based statistical machine translation -LRB- MT -RRB-", "start": 47, "end": 106}}], "compare": [{"head": {"text": "it", "start": 178, "end": 180}, "tail": {"text": "MT systems", "start": 190, "end": 200}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using this visualization method , we can find and address conceptual and practical problems in an MT system .", "output": {"relations": {"used for": [{"head": {"text": "visualization method", "start": 11, "end": 31}, "tail": {"text": "MT system", "start": 98, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 2, "end": 8}, "tail": {"text": "sense resolution", "start": 12, "end": 28}}, {"head": {"text": "WordNet", "start": 58, "end": 65}, "tail": {"text": "method", "start": 2, "end": 8}}], "hyponym of": [{"head": {"text": "WordNet", "start": 58, "end": 65}, "tail": {"text": "on-line lexical database", "start": 71, "end": 95}}, {"head": {"text": "synonymy", "start": 139, "end": 147}, "tail": {"text": "semantic relations", "start": 114, "end": 132}}, {"head": {"text": "antonymy", "start": 150, "end": 158}, "tail": {"text": "semantic relations", "start": 114, "end": 132}}, {"head": {"text": "hyponymy", "start": 161, "end": 169}, "tail": {"text": "semantic relations", "start": 114, "end": 132}}, {"head": {"text": "meronymy", "start": 172, "end": 180}, "tail": {"text": "semantic relations", "start": 114, "end": 132}}, {"head": {"text": "causal and troponymic entailment", "start": 183, "end": 215}, "tail": {"text": "semantic relations", "start": 114, "end": 132}}], "part of": [{"head": {"text": "semantic relations", "start": 114, "end": 132}, "tail": {"text": "WordNet", "start": 58, "end": 65}}], "conjunction": [{"head": {"text": "synonymy", "start": 139, "end": 147}, "tail": {"text": "antonymy", "start": 150, "end": 158}}, {"head": {"text": "antonymy", "start": 150, "end": 158}, "tail": {"text": "hyponymy", "start": 161, "end": 169}}, {"head": {"text": "hyponymy", "start": 161, "end": 169}, "tail": {"text": "meronymy", "start": 172, "end": 180}}, {"head": {"text": "meronymy", "start": 172, "end": 180}, "tail": {"text": "causal and troponymic entailment", "start": 183, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With WordNet , it is easy to retrieve sets of semantically related words , a facility that will be used for sense resolution during text processing , as follows .", "output": {"relations": {"used for": [{"head": {"text": "WordNet", "start": 5, "end": 12}, "tail": {"text": "semantically related words", "start": 46, "end": 72}}, {"head": {"text": "semantically related words", "start": 46, "end": 72}, "tail": {"text": "sense resolution", "start": 108, "end": 124}}, {"head": {"text": "sense resolution", "start": 108, "end": 124}, "tail": {"text": "text processing", "start": 132, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this procedure could have practical applications to problems of information retrieval , mechanical translation , intelligent tutoring systems , and elsewhere .", "output": {"relations": {"used for": [{"head": {"text": "WordNet", "start": 160, "end": 167}, "tail": {"text": "semantic distance", "start": 202, "end": 219}}, {"head": {"text": "procedure", "start": 411, "end": 420}, "tail": {"text": "information retrieval", "start": 470, "end": 491}}, {"head": {"text": "procedure", "start": 411, "end": 420}, "tail": {"text": "mechanical translation", "start": 494, "end": 516}}, {"head": {"text": "procedure", "start": 411, "end": 420}, "tail": {"text": "intelligent tutoring systems", "start": 519, "end": 547}}], "conjunction": [{"head": {"text": "information retrieval", "start": 470, "end": 491}, "tail": {"text": "mechanical translation", "start": 494, "end": 516}}, {"head": {"text": "mechanical translation", "start": 494, "end": 516}, "tail": {"text": "intelligent tutoring systems", "start": 519, "end": 547}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The interlingual approach to MT has been repeatedly advocated by researchers originally interested in natural language understanding who take machine translation to be one possible application .", "output": {"relations": {"used for": [{"head": {"text": "interlingual approach", "start": 4, "end": 25}, "tail": {"text": "MT", "start": 29, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In contrast , our project , the Mu-project , adopts the transfer approach as the basic framework of MT .", "output": {"relations": {"used for": [{"head": {"text": "Mu-project", "start": 32, "end": 42}, "tail": {"text": "MT", "start": 100, "end": 102}}, {"head": {"text": "transfer approach", "start": 56, "end": 73}, "tail": {"text": "Mu-project", "start": 32, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes the detailed construction of the transfer phase of our system from Japanese to English , and gives some examples of problems which seem difficult to treat in the interlingual approach .", "output": {"relations": {"part of": [{"head": {"text": "transfer phase", "start": 54, "end": 68}, "tail": {"text": "system", "start": 76, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The basic design principles of the transfer phase of our system have already been mentioned in -LRB- 1 -RRB- -LRB- 2 -RRB- .", "output": {"relations": {"part of": [{"head": {"text": "transfer phase", "start": 35, "end": 49}, "tail": {"text": "system", "start": 57, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Some of the principles which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- Form-Oriented Dictionary Description .", "output": {"relations": {"part of": [{"head": {"text": "Multiple Layer of Grammars", "start": 89, "end": 115}, "tail": {"text": "principles", "start": 12, "end": 22}}, {"head": {"text": "Multiple Layer Presentation", "start": 130, "end": 157}, "tail": {"text": "principles", "start": 12, "end": 22}}, {"head": {"text": "Lexicon Driven Processing", "start": 172, "end": 197}, "tail": {"text": "principles", "start": 12, "end": 22}}, {"head": {"text": "Form-Oriented Dictionary Description", "start": 212, "end": 248}, "tail": {"text": "principles", "start": 12, "end": 22}}], "conjunction": [{"head": {"text": "Multiple Layer of Grammars", "start": 89, "end": 115}, "tail": {"text": "Multiple Layer Presentation", "start": 130, "end": 157}}, {"head": {"text": "Multiple Layer Presentation", "start": 130, "end": 157}, "tail": {"text": "Lexicon Driven Processing", "start": 172, "end": 197}}, {"head": {"text": "Lexicon Driven Processing", "start": 172, "end": 197}, "tail": {"text": "Form-Oriented Dictionary Description", "start": 212, "end": 248}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper also shows how these principles are realized in the current system .", "output": {"relations": {"part of": [{"head": {"text": "principles", "start": 32, "end": 42}, "tail": {"text": "system", "start": 71, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper discourse segments are defined and a method for discourse segmentation primarily based on abduction of temporal relations between segments is proposed .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 51, "end": 57}, "tail": {"text": "discourse segmentation", "start": 62, "end": 84}}, {"head": {"text": "abduction of temporal relations", "start": 104, "end": 135}, "tail": {"text": "discourse segmentation", "start": 62, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This method is precise and computationally feasible and is supported by previous work in the area of temporal anaphora resolution .", "output": {"relations": {"used for": [{"head": {"text": "temporal anaphora resolution", "start": 101, "end": 129}, "tail": {"text": "method", "start": 5, "end": 11}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes to what extent deep processing may benefit from shallow techniques and it presents a NLP system which integrates a linguistic PoS tagger and chunker as a preprocessing module of a broad coverage unification based grammar of Spanish .", "output": {"relations": {"used for": [{"head": {"text": "shallow techniques", "start": 69, "end": 87}, "tail": {"text": "deep processing", "start": 36, "end": 51}}, {"head": {"text": "broad coverage unification based grammar of Spanish", "start": 201, "end": 252}, "tail": {"text": "NLP system", "start": 106, "end": 116}}], "part of": [{"head": {"text": "linguistic PoS tagger and chunker", "start": 136, "end": 169}, "tail": {"text": "broad coverage unification based grammar of Spanish", "start": 201, "end": 252}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments show that the efficiency of the overall analysis improves significantly and that our system also provides robustness to the linguistic processing while maintaining both the accuracy and the precision of the grammar .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 97, "end": 103}, "tail": {"text": "linguistic processing", "start": 136, "end": 157}}], "evaluate for": [{"head": {"text": "robustness", "start": 118, "end": 128}, "tail": {"text": "system", "start": 97, "end": 103}}, {"head": {"text": "accuracy", "start": 185, "end": 193}, "tail": {"text": "system", "start": 97, "end": 103}}, {"head": {"text": "precision", "start": 202, "end": 211}, "tail": {"text": "system", "start": 97, "end": 103}}], "conjunction": [{"head": {"text": "accuracy", "start": 185, "end": 193}, "tail": {"text": "precision", "start": 202, "end": 211}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Joint image filters can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution .", "output": {"relations": {"used for": [{"head": {"text": "Joint image filters", "start": 0, "end": 19}, "tail": {"text": "suppressing noise", "start": 147, "end": 164}}, {"head": {"text": "Joint image filters", "start": 0, "end": 19}, "tail": {"text": "enhancing spatial resolution", "start": 168, "end": 196}}, {"head": {"text": "guidance image", "start": 37, "end": 51}, "tail": {"text": "Joint image filters", "start": 0, "end": 19}}], "conjunction": [{"head": {"text": "suppressing noise", "start": 147, "end": 164}, "tail": {"text": "enhancing spatial resolution", "start": 168, "end": 196}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Existing methods rely on various kinds of explicit filter construction or hand-designed objective functions .", "output": {"relations": {"used for": [{"head": {"text": "explicit filter construction", "start": 42, "end": 70}, "tail": {"text": "Existing methods", "start": 0, "end": 16}}, {"head": {"text": "hand-designed objective functions", "start": 74, "end": 107}, "tail": {"text": "Existing methods", "start": 0, "end": 16}}], "conjunction": [{"head": {"text": "explicit filter construction", "start": 42, "end": 70}, "tail": {"text": "hand-designed objective functions", "start": 74, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is thus difficult to understand , improve , and accelerate them in a coherent framework .", "output": {"relations": {"used for": [{"head": {"text": "coherent framework", "start": 72, "end": 90}, "tail": {"text": "them", "start": 62, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a learning-based approach to construct a joint filter based on Convolution-al Neural Networks .", "output": {"relations": {"used for": [{"head": {"text": "learning-based approach", "start": 29, "end": 52}, "tail": {"text": "joint filter", "start": 68, "end": 80}}, {"head": {"text": "Convolution-al Neural Networks", "start": 90, "end": 120}, "tail": {"text": "learning-based approach", "start": 29, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In contrast to existing methods that consider only the guidance image , our method can selectively transfer salient structures that are consistent in both guidance and target images .", "output": {"relations": {"compare": [{"head": {"text": "methods", "start": 24, "end": 31}, "tail": {"text": "method", "start": 24, "end": 30}}], "used for": [{"head": {"text": "guidance image", "start": 55, "end": 69}, "tail": {"text": "methods", "start": 24, "end": 31}}, {"head": {"text": "method", "start": 24, "end": 30}, "tail": {"text": "transfer salient structures", "start": 99, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the model trained on a certain type of data , e.g. , RGB and depth images , generalizes well for other modalities , e.g. , Flash/Non-Flash and RGB/NIR images .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 17, "end": 22}, "tail": {"text": "modalities", "start": 116, "end": 126}}, {"head": {"text": "data", "start": 52, "end": 56}, "tail": {"text": "model", "start": 17, "end": 22}}], "hyponym of": [{"head": {"text": "RGB and depth images", "start": 66, "end": 86}, "tail": {"text": "data", "start": 52, "end": 56}}, {"head": {"text": "Flash/Non-Flash and RGB/NIR images", "start": 136, "end": 170}, "tail": {"text": "modalities", "start": 116, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We validate the effectiveness of the proposed joint filter through extensive comparisons with state-of-the-art methods .", "output": {"relations": {"compare": [{"head": {"text": "joint filter", "start": 46, "end": 58}, "tail": {"text": "state-of-the-art methods", "start": 94, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our current research into the design of cognitively well-motivated interfaces relying primarily on the display of graphical information , we have observed that graphical information alone does not provide sufficient support to users - particularly when situations arise that do not simply conform to the users ' expectations .", "output": {"relations": {"used for": [{"head": {"text": "display of graphical information", "start": 106, "end": 138}, "tail": {"text": "cognitively well-motivated interfaces", "start": 43, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To solve this problem , we are working towards the integration of natural language generation to augment the interaction", "output": {"relations": {"used for": [{"head": {"text": "natural language generation", "start": 66, "end": 93}, "tail": {"text": "interaction", "start": 109, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A central problem of word sense disambiguation -LRB- WSD -RRB- is the lack of manually sense-tagged data required for supervised learning .", "output": {"relations": {"used for": [{"head": {"text": "manually sense-tagged data", "start": 78, "end": 104}, "tail": {"text": "supervised learning", "start": 118, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora , which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 31, "end": 39}, "tail": {"text": "sense-tagged training data", "start": 65, "end": 91}}, {"head": {"text": "approach", "start": 31, "end": 39}, "tail": {"text": "nouns", "start": 175, "end": 180}}, {"head": {"text": "English-Chinese parallel corpora", "start": 97, "end": 129}, "tail": {"text": "sense-tagged training data", "start": 65, "end": 91}}], "part of": [{"head": {"text": "nouns", "start": 175, "end": 180}, "tail": {"text": "SENSEVAL-2 English lexical sample task", "start": 188, "end": 226}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our investigation reveals that this method of acquiring sense-tagged data is promising .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 36, "end": 42}, "tail": {"text": "acquiring sense-tagged data", "start": 46, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "On a subset of the most difficult SENSEVAL-2 nouns , the accuracy difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that manually sense-tagged data have in their sense coverage .", "output": {"relations": {"feature of": [{"head": {"text": "sense coverage", "start": 246, "end": 260}, "tail": {"text": "manually sense-tagged data", "start": 205, "end": 231}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs .", "output": {"relations": {"feature of": [{"head": {"text": "domain dependence", "start": 60, "end": 77}, "tail": {"text": "evaluating WSD programs", "start": 81, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an analysis of temporal anaphora in sentences which contain quantification over events , within the framework of Discourse Representation Theory .", "output": {"relations": {"part of": [{"head": {"text": "quantification over events", "start": 80, "end": 106}, "tail": {"text": "temporal anaphora", "start": 35, "end": 52}}], "used for": [{"head": {"text": "Discourse Representation Theory", "start": 133, "end": 164}, "tail": {"text": "temporal anaphora", "start": 35, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The analysis in -LRB- Partee , 1984 -RRB- of quantified sentences , introduced by a temporal connective , gives the wrong truth-conditions when the temporal connective in the subordinate clause is before or after .", "output": {"relations": {"part of": [{"head": {"text": "temporal connective", "start": 84, "end": 103}, "tail": {"text": "subordinate clause", "start": 175, "end": 193}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This problem has been previously analyzed in -LRB- de Swart , 1991 -RRB- as an instance of the proportion problem and given a solution from a Generalized Quantifier approach .", "output": {"relations": {"used for": [{"head": {"text": "Generalized Quantifier approach", "start": 142, "end": 173}, "tail": {"text": "problem", "start": 5, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By using a careful distinction between the different notions of reference time based on -LRB- Kamp and Reyle , 1993 -RRB- , we propose a solution to this problem , within the framework of DRT .", "output": {"relations": {"used for": [{"head": {"text": "solution", "start": 137, "end": 145}, "tail": {"text": "problem", "start": 154, "end": 161}}, {"head": {"text": "DRT", "start": 188, "end": 191}, "tail": {"text": "problem", "start": 154, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show some applications of this solution to additional temporal anaphora phenomena in quantified sentences .", "output": {"relations": {"used for": [{"head": {"text": "solution", "start": 34, "end": 42}, "tail": {"text": "temporal anaphora phenomena", "start": 57, "end": 84}}, {"head": {"text": "quantified sentences", "start": 88, "end": 108}, "tail": {"text": "solution", "start": 34, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we explore correlation of dependency relation paths to rank candidate answers in answer extraction .", "output": {"relations": {"used for": [{"head": {"text": "correlation of dependency relation paths", "start": 27, "end": 67}, "tail": {"text": "answer extraction", "start": 97, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using the correlation measure , we compare dependency relations of a candidate answer and mapped question phrases in sentence with the corresponding relations in question .", "output": {"relations": {"used for": [{"head": {"text": "correlation measure", "start": 10, "end": 29}, "tail": {"text": "dependency relations", "start": 43, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Different from previous studies , we propose an approximate phrase mapping algorithm and incorporate the mapping score into the correlation measure .", "output": {"relations": {"part of": [{"head": {"text": "mapping score", "start": 105, "end": 118}, "tail": {"text": "correlation measure", "start": 128, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The correlations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training .", "output": {"relations": {"part of": [{"head": {"text": "correlations", "start": 4, "end": 16}, "tail": {"text": "Maximum Entropy-based ranking model", "start": 49, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results show that our method significantly outperforms state-of-the-art syntactic relation-based methods by up to 20 % in MRR .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 35, "end": 41}, "tail": {"text": "syntactic relation-based methods", "start": 85, "end": 117}}], "evaluate for": [{"head": {"text": "MRR", "start": 135, "end": 138}, "tail": {"text": "method", "start": 35, "end": 41}}, {"head": {"text": "MRR", "start": 135, "end": 138}, "tail": {"text": "syntactic relation-based methods", "start": 85, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Evaluation is also crucial to assessing competing claims and identifying promising technical approaches .", "output": {"relations": {"used for": [{"head": {"text": "Evaluation", "start": 0, "end": 10}, "tail": {"text": "approaches", "start": 93, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems -LRB- SLS -RRB- program to agree on a methodology for comparative evaluation of SLS systems , and that methodology has been put into practice several times in comparative tests of several SLS systems .", "output": {"relations": {"evaluate for": [{"head": {"text": "methodology", "start": 151, "end": 162}, "tail": {"text": "evaluation of SLS systems", "start": 179, "end": 204}}, {"head": {"text": "methodology", "start": 151, "end": 162}, "tail": {"text": "SLS systems", "start": 193, "end": 204}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These evaluations are probably the only NL evaluations other than the series of Message Understanding Conferences -LRB- Sundheim , 1989 ; Sundheim , 1991 -RRB- to have been developed and used by a group of researchers at different sites , although several excellent workshops have been held to study some of these problems -LRB- Palmer et al. , 1989 ; Neal et al. , 1991 -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "evaluations", "start": 6, "end": 17}, "tail": {"text": "NL evaluations", "start": 40, "end": 54}}], "conjunction": [{"head": {"text": "evaluations", "start": 6, "end": 17}, "tail": {"text": "Message Understanding Conferences", "start": 80, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a practical `` black-box '' methodology for automatic evaluation of question-answering NL systems .", "output": {"relations": {"evaluate for": [{"head": {"text": "`` black-box '' methodology", "start": 33, "end": 60}, "tail": {"text": "automatic evaluation of question-answering NL systems", "start": 65, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While each new application domain will require some development of special resources , the heart of the methodology is domain-independent , and it can be used with either speech or text input .", "output": {"relations": {"used for": [{"head": {"text": "speech or text input", "start": 171, "end": 191}, "tail": {"text": "it", "start": 144, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we present a novel autonomous pipeline to build a personalized parametric model -LRB- pose-driven avatar -RRB- using a single depth sensor .", "output": {"relations": {"used for": [{"head": {"text": "autonomous pipeline", "start": 33, "end": 52}, "tail": {"text": "personalized parametric model -LRB- pose-driven avatar -RRB-", "start": 64, "end": 124}}, {"head": {"text": "single depth sensor", "start": 133, "end": 152}, "tail": {"text": "autonomous pipeline", "start": 33, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We fit each incomplete scan using template fitting techniques with a generic human template , and register all scans to every pose using global consistency constraints .", "output": {"relations": {"used for": [{"head": {"text": "human template", "start": 77, "end": 91}, "tail": {"text": "template fitting techniques", "start": 34, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After registration , these watertight models with different poses are used to train a parametric model in a fashion similar to the SCAPE method .", "output": {"relations": {"used for": [{"head": {"text": "watertight models", "start": 27, "end": 44}, "tail": {"text": "parametric model", "start": 86, "end": 102}}, {"head": {"text": "SCAPE method", "start": 131, "end": 143}, "tail": {"text": "parametric model", "start": 86, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Once the parametric model is built , it can be used as an anim-itable avatar or more interestingly synthesizing dynamic 3D models from single-view depth videos .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 37, "end": 39}, "tail": {"text": "anim-itable avatar", "start": 58, "end": 76}}, {"head": {"text": "it", "start": 37, "end": 39}, "tail": {"text": "dynamic 3D models", "start": 112, "end": 129}}, {"head": {"text": "single-view depth videos", "start": 135, "end": 159}, "tail": {"text": "dynamic 3D models", "start": 112, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results demonstrate the effectiveness of our system to produce dynamic models .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 58, "end": 64}, "tail": {"text": "dynamic models", "start": 76, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a novel algorithm to detect/compensate on-line interference effects when integrating Global Navigation Satellite System -LRB- GNSS -RRB- and Inertial Navigation System -LRB- INS -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 35, "end": 44}, "tail": {"text": "on-line interference effects", "start": 66, "end": 94}}], "conjunction": [{"head": {"text": "Global Navigation Satellite System -LRB- GNSS -RRB-", "start": 112, "end": 163}, "tail": {"text": "Inertial Navigation System -LRB- INS -RRB-", "start": 168, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The GNSS/INS coupling is usually performed by an Extended Kalman Filter -LRB- EKF -RRB- which yields an accurate and robust localization .", "output": {"relations": {"used for": [{"head": {"text": "Extended Kalman Filter -LRB- EKF -RRB-", "start": 49, "end": 87}, "tail": {"text": "GNSS/INS coupling", "start": 4, "end": 21}}, {"head": {"text": "Extended Kalman Filter -LRB- EKF -RRB-", "start": 49, "end": 87}, "tail": {"text": "accurate and robust localization", "start": 104, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We first study the impact of the GNSS noise inflation on the covariance of the EKF outputs so as to compute a least square estimate of the potential variance jumps .", "output": {"relations": {"feature of": [{"head": {"text": "EKF outputs", "start": 79, "end": 90}, "tail": {"text": "covariance", "start": 61, "end": 71}}], "used for": [{"head": {"text": "least square estimate", "start": 110, "end": 131}, "tail": {"text": "variance jumps", "start": 149, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Then , this estimation is used in a Bayesian test which decides whether interference are corrupting the GNSS signal or not .", "output": {"relations": {"used for": [{"head": {"text": "estimation", "start": 12, "end": 22}, "tail": {"text": "Bayesian test", "start": 36, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The results show the performance of the proposed approach on simulated data .", "output": {"relations": {"evaluate for": [{"head": {"text": "simulated data", "start": 61, "end": 75}, "tail": {"text": "approach", "start": 49, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a unified variational formulation for joint motion estimation and segmentation with explicit occlusion handling .", "output": {"relations": {"used for": [{"head": {"text": "unified variational formulation", "start": 13, "end": 44}, "tail": {"text": "joint motion estimation and segmentation", "start": 49, "end": 89}}, {"head": {"text": "explicit occlusion handling", "start": 95, "end": 122}, "tail": {"text": "unified variational formulation", "start": 13, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints .", "output": {"relations": {"used for": [{"head": {"text": "convex formulation", "start": 9, "end": 27}, "tail": {"text": "multi-label Potts model", "start": 35, "end": 58}}, {"head": {"text": "convex constraints", "start": 181, "end": 199}, "tail": {"text": "formulation", "start": 16, "end": 27}}], "part of": [{"head": {"text": "asymmetric map-uniqueness criterion", "start": 94, "end": 129}, "tail": {"text": "formulation", "start": 16, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By using a fast primal-dual algorithm we are able to handle several hundred motion segments .", "output": {"relations": {"used for": [{"head": {"text": "primal-dual algorithm", "start": 16, "end": 37}, "tail": {"text": "motion segments", "start": 76, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Two main classes of approaches have been studied to perform monocular nonrigid 3D reconstruction : Template-based methods and Non-rigid Structure from Motion techniques .", "output": {"relations": {"used for": [{"head": {"text": "approaches", "start": 20, "end": 30}, "tail": {"text": "monocular nonrigid 3D reconstruction", "start": 60, "end": 96}}], "hyponym of": [{"head": {"text": "Template-based methods", "start": 99, "end": 121}, "tail": {"text": "approaches", "start": 20, "end": 30}}, {"head": {"text": "Non-rigid Structure from Motion techniques", "start": 126, "end": 168}, "tail": {"text": "approaches", "start": 20, "end": 30}}], "conjunction": [{"head": {"text": "Template-based methods", "start": 99, "end": 121}, "tail": {"text": "Non-rigid Structure from Motion techniques", "start": 126, "end": 168}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While the first ones have been applied to reconstruct poorly-textured surfaces , they assume the availability of a 3D shape model prior to reconstruction .", "output": {"relations": {"used for": [{"head": {"text": "ones", "start": 16, "end": 20}, "tail": {"text": "poorly-textured surfaces", "start": 54, "end": 78}}, {"head": {"text": "3D shape model", "start": 115, "end": 129}, "tail": {"text": "they", "start": 81, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we introduce a template-free approach to reconstructing a poorly-textured , deformable surface .", "output": {"relations": {"used for": [{"head": {"text": "template-free approach", "start": 31, "end": 53}, "tail": {"text": "poorly-textured , deformable surface", "start": 74, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To this end , we leverage surface isometry and formulate 3D reconstruction as the joint problem of non-rigid image registration and depth estimation .", "output": {"relations": {"used for": [{"head": {"text": "surface isometry", "start": 26, "end": 42}, "tail": {"text": "3D reconstruction", "start": 57, "end": 74}}, {"head": {"text": "joint problem of non-rigid image registration and depth estimation", "start": 82, "end": 148}, "tail": {"text": "3D reconstruction", "start": 57, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments demonstrate that our approach yields much more accurate 3D reconstructions than state-of-the-art techniques .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 37, "end": 45}, "tail": {"text": "state-of-the-art techniques", "start": 96, "end": 123}}], "evaluate for": [{"head": {"text": "3D reconstructions", "start": 72, "end": 90}, "tail": {"text": "approach", "start": 37, "end": 45}}, {"head": {"text": "3D reconstructions", "start": 72, "end": 90}, "tail": {"text": "state-of-the-art techniques", "start": 96, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Many computer vision applications , such as image classification and video indexing , are usually multi-label classification problems in which an instance can be assigned to more than one category .", "output": {"relations": {"hyponym of": [{"head": {"text": "image classification", "start": 44, "end": 64}, "tail": {"text": "computer vision applications", "start": 5, "end": 33}}, {"head": {"text": "video indexing", "start": 69, "end": 83}, "tail": {"text": "computer vision applications", "start": 5, "end": 33}}], "conjunction": [{"head": {"text": "image classification", "start": 44, "end": 64}, "tail": {"text": "video indexing", "start": 69, "end": 83}}], "used for": [{"head": {"text": "multi-label classification problems", "start": 98, "end": 133}, "tail": {"text": "computer vision applications", "start": 5, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a novel multi-label classification approach with hypergraph regu-larization that addresses the correlations among different categories .", "output": {"relations": {"feature of": [{"head": {"text": "hypergraph regu-larization", "start": 76, "end": 102}, "tail": {"text": "multi-label classification approach", "start": 35, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Then , an improved SVM like learning system incorporating the hypergraph regularization , called Rank-HLapSVM , is proposed to handle the multi-label classification problems .", "output": {"relations": {"used for": [{"head": {"text": "SVM like learning system", "start": 19, "end": 43}, "tail": {"text": "multi-label classification problems", "start": 138, "end": 173}}], "part of": [{"head": {"text": "hypergraph regularization", "start": 62, "end": 87}, "tail": {"text": "SVM like learning system", "start": 19, "end": 43}}], "hyponym of": [{"head": {"text": "Rank-HLapSVM", "start": 97, "end": 109}, "tail": {"text": "SVM like learning system", "start": 19, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method .", "output": {"relations": {"used for": [{"head": {"text": "dual coordinate descent method", "start": 85, "end": 115}, "tail": {"text": "optimization problem", "start": 31, "end": 51}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Many promising experimental results on the real datasets including ImageCLEF and Me-diaMill demonstrate the effectiveness and efficiency of the proposed algorithm .", "output": {"relations": {"evaluate for": [{"head": {"text": "real datasets", "start": 43, "end": 56}, "tail": {"text": "algorithm", "start": 153, "end": 162}}], "hyponym of": [{"head": {"text": "ImageCLEF", "start": 67, "end": 76}, "tail": {"text": "real datasets", "start": 43, "end": 56}}, {"head": {"text": "Me-diaMill", "start": 81, "end": 91}, "tail": {"text": "real datasets", "start": 43, "end": 56}}], "conjunction": [{"head": {"text": "ImageCLEF", "start": 67, "end": 76}, "tail": {"text": "Me-diaMill", "start": 81, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We derive a convex optimization problem for the task of segmenting sequential data , which explicitly treats presence of outliers .", "output": {"relations": {"used for": [{"head": {"text": "convex optimization problem", "start": 12, "end": 39}, "tail": {"text": "segmenting sequential data", "start": 56, "end": 82}}, {"head": {"text": "segmenting sequential data", "start": 56, "end": 82}, "tail": {"text": "outliers", "start": 121, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe two algorithms for solving this problem , one exact and one a top-down novel approach , and we derive a consistency results for the case of two segments and no outliers .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 16, "end": 26}, "tail": {"text": "problem", "start": 44, "end": 51}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Robustness to outliers is evaluated on two real-world tasks related to speech segmentation .", "output": {"relations": {"feature of": [{"head": {"text": "outliers", "start": 14, "end": 22}, "tail": {"text": "Robustness", "start": 0, "end": 10}}, {"head": {"text": "speech segmentation", "start": 71, "end": 90}, "tail": {"text": "real-world tasks", "start": 43, "end": 59}}], "evaluate for": [{"head": {"text": "real-world tasks", "start": 43, "end": 59}, "tail": {"text": "Robustness", "start": 0, "end": 10}}, {"head": {"text": "speech segmentation", "start": 71, "end": 90}, "tail": {"text": "Robustness", "start": 0, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our algorithms outperform baseline seg-mentation algorithms .", "output": {"relations": {"compare": [{"head": {"text": "algorithms", "start": 4, "end": 14}, "tail": {"text": "baseline seg-mentation algorithms", "start": 26, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper examines the properties of feature-based partial descriptions built on top of Halliday 's systemic networks .", "output": {"relations": {"used for": [{"head": {"text": "Halliday 's systemic networks", "start": 89, "end": 118}, "tail": {"text": "feature-based partial descriptions", "start": 38, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the crucial operation of consistency checking for such descriptions is NP-complete , and therefore probably intractable , but proceed to develop algorithms which can sometimes alleviate the unpleasant consequences of this intractability .", "output": {"relations": {"used for": [{"head": {"text": "consistency checking", "start": 38, "end": 58}, "tail": {"text": "descriptions", "start": 68, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe Yoopick , a combinatorial sports prediction market that implements a flexible betting language , and in turn facilitates fine-grained probabilistic estimation of outcomes .", "output": {"relations": {"hyponym of": [{"head": {"text": "Yoopick", "start": 12, "end": 19}, "tail": {"text": "combinatorial sports prediction market", "start": 24, "end": 62}}], "used for": [{"head": {"text": "Yoopick", "start": 12, "end": 19}, "tail": {"text": "fine-grained probabilistic estimation of outcomes", "start": 133, "end": 182}}, {"head": {"text": "flexible betting language", "start": 81, "end": 106}, "tail": {"text": "Yoopick", "start": 12, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation .", "output": {"relations": {"used for": [{"head": {"text": "discriminative patches", "start": 47, "end": 69}, "tail": {"text": "unsupervised mid-level visual representation", "start": 97, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches .", "output": {"relations": {"used for": [{"head": {"text": "image patches", "start": 87, "end": 100}, "tail": {"text": "unsupervised discriminative clustering problem", "start": 19, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We use an iterative procedure which alternates between clustering and training discriminative classifiers , while applying careful cross-validation at each step to prevent overfitting .", "output": {"relations": {"used for": [{"head": {"text": "cross-validation", "start": 131, "end": 147}, "tail": {"text": "overfitting", "start": 172, "end": 183}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation , suggesting that it could be used in place of visual words for many tasks .", "output": {"relations": {"used for": [{"head": {"text": "discriminative patches", "start": 59, "end": 81}, "tail": {"text": "unsupervised mid-level visual representation", "start": 88, "end": 132}}, {"head": {"text": "it", "start": 151, "end": 153}, "tail": {"text": "discriminative patches", "start": 59, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , discrim-inative patches can also be used in a supervised regime , such as scene classification , where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset .", "output": {"relations": {"used for": [{"head": {"text": "discrim-inative patches", "start": 14, "end": 37}, "tail": {"text": "supervised regime", "start": 60, "end": 77}}], "hyponym of": [{"head": {"text": "scene classification", "start": 88, "end": 108}, "tail": {"text": "supervised regime", "start": 60, "end": 77}}], "evaluate for": [{"head": {"text": "MIT Indoor-67 dataset", "start": 170, "end": 191}, "tail": {"text": "they", "start": 117, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We investigate the utility of an algorithm for translation lexicon acquisition -LRB- SABLE -RRB- , used previously on a very large corpus to acquire general translation lexicons , when that algorithm is applied to a much smaller corpus to produce candidates for domain-specific translation lexicons .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 33, "end": 42}, "tail": {"text": "translation lexicon acquisition -LRB- SABLE -RRB-", "start": 47, "end": 96}}, {"head": {"text": "algorithm", "start": 33, "end": 42}, "tail": {"text": "general translation lexicons", "start": 149, "end": 177}}, {"head": {"text": "algorithm", "start": 33, "end": 42}, "tail": {"text": "domain-specific translation lexicons", "start": 262, "end": 298}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a computational model of word segmentation and presents simulation results on realistic acquisition .", "output": {"relations": {"used for": [{"head": {"text": "computational model", "start": 23, "end": 42}, "tail": {"text": "word segmentation", "start": 46, "end": 63}}, {"head": {"text": "computational model", "start": 23, "end": 42}, "tail": {"text": "realistic acquisition", "start": 99, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , we explore the capacity and limitations of statistical learning mechanisms that have recently gained prominence in cognitive psychology and linguistics .", "output": {"relations": {"used for": [{"head": {"text": "cognitive psychology", "start": 131, "end": 151}, "tail": {"text": "statistical learning mechanisms", "start": 59, "end": 90}}, {"head": {"text": "linguistics", "start": 156, "end": 167}, "tail": {"text": "statistical learning mechanisms", "start": 59, "end": 90}}], "conjunction": [{"head": {"text": "cognitive psychology", "start": 131, "end": 151}, "tail": {"text": "linguistics", "start": 156, "end": 167}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the model-based policy search approach to reinforcement learning -LRB- RL -RRB- , policies are found using a model -LRB- or `` simulator '' -RRB- of the Markov decision process .", "output": {"relations": {"used for": [{"head": {"text": "model-based policy search approach", "start": 7, "end": 41}, "tail": {"text": "reinforcement learning -LRB- RL -RRB-", "start": 45, "end": 82}}, {"head": {"text": "Markov decision process", "start": 156, "end": 179}, "tail": {"text": "policies", "start": 85, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , for high-dimensional continuous-state tasks , it can be extremely difficult to build an accurate model , and thus often the algorithm returns a policy that works in simulation but not in real-life .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 107, "end": 112}, "tail": {"text": "high-dimensional continuous-state tasks", "start": 14, "end": 53}}, {"head": {"text": "algorithm", "start": 134, "end": 143}, "tail": {"text": "policy", "start": 154, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The other extreme , model-free RL , tends to require infeasibly large numbers of real-life trials .", "output": {"relations": {"used for": [{"head": {"text": "real-life trials", "start": 81, "end": 97}, "tail": {"text": "model-free RL", "start": 20, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a hybrid algorithm that requires only an approximate model , and only a small number of real-life trials .", "output": {"relations": {"used for": [{"head": {"text": "approximate model", "start": 68, "end": 85}, "tail": {"text": "hybrid algorithm", "start": 29, "end": 45}}, {"head": {"text": "real-life trials", "start": 115, "end": 131}, "tail": {"text": "approximate model", "start": 68, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The key idea is to successively `` ground '' the policy evaluations using real-life trials , but to rely on the approximate model to suggest local changes .", "output": {"relations": {"used for": [{"head": {"text": "real-life trials", "start": 74, "end": 90}, "tail": {"text": "policy evaluations", "start": 49, "end": 67}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Empirical results also demonstrate that -- when given only a crude model and a small number of real-life trials -- our algorithm can obtain near-optimal performance in the real system .", "output": {"relations": {"conjunction": [{"head": {"text": "crude model", "start": 61, "end": 72}, "tail": {"text": "real-life trials", "start": 95, "end": 111}}], "used for": [{"head": {"text": "crude model", "start": 61, "end": 72}, "tail": {"text": "algorithm", "start": 119, "end": 128}}, {"head": {"text": "real-life trials", "start": 95, "end": 111}, "tail": {"text": "algorithm", "start": 119, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although every natural language system needs a computational lexicon , each system puts different amounts and types of information into its lexicon according to its individual needs .", "output": {"relations": {"used for": [{"head": {"text": "computational lexicon", "start": 47, "end": 68}, "tail": {"text": "natural language system", "start": 15, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents our experience in planning and building COMPLEX , a computational lexicon designed to be a repository of shared lexical information for use by Natural Language Processing -LRB- NLP -RRB- systems .", "output": {"relations": {"hyponym of": [{"head": {"text": "COMPLEX", "start": 60, "end": 67}, "tail": {"text": "computational lexicon", "start": 72, "end": 93}}], "used for": [{"head": {"text": "COMPLEX", "start": 60, "end": 67}, "tail": {"text": "Natural Language Processing -LRB- NLP -RRB- systems", "start": 163, "end": 214}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Sentence planning is a set of inter-related but distinct tasks , one of which is sentence scoping , i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences .", "output": {"relations": {"part of": [{"head": {"text": "sentence scoping", "start": 81, "end": 97}, "tail": {"text": "tasks", "start": 57, "end": 62}}], "used for": [{"head": {"text": "syntactic structure", "start": 119, "end": 138}, "tail": {"text": "speech acts", "start": 154, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present SPoT , a sentence planner , and a new methodology for automatically training SPoT on the basis of feedback provided by human judges .", "output": {"relations": {"hyponym of": [{"head": {"text": "SPoT", "start": 27, "end": 31}, "tail": {"text": "sentence planner", "start": 36, "end": 52}}], "used for": [{"head": {"text": "methodology", "start": 65, "end": 76}, "tail": {"text": "SPoT", "start": 27, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , a very simple , randomized sentence-plan-generator -LRB- SPG -RRB- generates a potentially large list of possible sentence plans for a given text-plan input .", "output": {"relations": {"used for": [{"head": {"text": "randomized sentence-plan-generator -LRB- SPG -RRB-", "start": 24, "end": 74}, "tail": {"text": "sentence plans", "start": 122, "end": 136}}, {"head": {"text": "text-plan input", "start": 149, "end": 164}, "tail": {"text": "randomized sentence-plan-generator -LRB- SPG -RRB-", "start": 24, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , the sentence-plan-ranker -LRB- SPR -RRB- ranks the list of output sentence plans , and then selects the top-ranked plan .", "output": {"relations": {"used for": [{"head": {"text": "sentence-plan-ranker -LRB- SPR -RRB-", "start": 13, "end": 49}, "tail": {"text": "sentence plans", "start": 75, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The SPR uses ranking rules automatically learned from training data .", "output": {"relations": {"used for": [{"head": {"text": "ranking rules", "start": 13, "end": 26}, "tail": {"text": "SPR", "start": 4, "end": 7}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the trained SPR learns to select a sentence plan whose rating on average is only 5 % worse than the top human-ranked sentence plan .", "output": {"relations": {"used for": [{"head": {"text": "SPR", "start": 25, "end": 28}, "tail": {"text": "sentence plan", "start": 48, "end": 61}}], "compare": [{"head": {"text": "sentence plan", "start": 48, "end": 61}, "tail": {"text": "top human-ranked sentence plan", "start": 113, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We discuss maximum a posteriori estimation of continuous density hidden Markov models -LRB- CDHMM -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "maximum a posteriori estimation", "start": 11, "end": 42}, "tail": {"text": "continuous density hidden Markov models -LRB- CDHMM -RRB-", "start": 46, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The classical MLE reestimation algorithms , namely the forward-backward algorithm and the segmental k-means algorithm , are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities .", "output": {"relations": {"hyponym of": [{"head": {"text": "forward-backward algorithm", "start": 55, "end": 81}, "tail": {"text": "MLE reestimation algorithms", "start": 14, "end": 41}}, {"head": {"text": "segmental k-means algorithm", "start": 90, "end": 117}, "tail": {"text": "MLE reestimation algorithms", "start": 14, "end": 41}}], "conjunction": [{"head": {"text": "segmental k-means algorithm", "start": 90, "end": 117}, "tail": {"text": "forward-backward algorithm", "start": 55, "end": 81}}], "used for": [{"head": {"text": "reestimation formulas", "start": 137, "end": 158}, "tail": {"text": "HMM with Gaussian mixture observation densities", "start": 173, "end": 220}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely parameter smoothing , speaker adaptation , speaker group modeling and corrective training .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian learning", "start": 33, "end": 50}, "tail": {"text": "speech recognition applications", "start": 103, "end": 134}}], "hyponym of": [{"head": {"text": "parameter smoothing", "start": 144, "end": 163}, "tail": {"text": "speech recognition applications", "start": 103, "end": 134}}, {"head": {"text": "speaker adaptation", "start": 166, "end": 184}, "tail": {"text": "speech recognition applications", "start": 103, "end": 134}}, {"head": {"text": "speaker group modeling", "start": 187, "end": 209}, "tail": {"text": "speech recognition applications", "start": 103, "end": 134}}, {"head": {"text": "corrective training", "start": 214, "end": 233}, "tail": {"text": "speech recognition applications", "start": 103, "end": 134}}], "conjunction": [{"head": {"text": "parameter smoothing", "start": 144, "end": 163}, "tail": {"text": "speaker adaptation", "start": 166, "end": 184}}, {"head": {"text": "speaker adaptation", "start": 166, "end": 184}, "tail": {"text": "speaker group modeling", "start": 187, "end": 209}}, {"head": {"text": "speaker group modeling", "start": 187, "end": 209}, "tail": {"text": "corrective training", "start": 214, "end": 233}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "New experimental results on all four applications are provided to show the effectiveness of the MAP estimation approach .", "output": {"relations": {"evaluate for": [{"head": {"text": "applications", "start": 37, "end": 49}, "tail": {"text": "MAP estimation approach", "start": 96, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a characters-based Chinese collocation system and discusses the advantages of it over a traditional word-based system .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 99, "end": 101}, "tail": {"text": "word-based system", "start": 121, "end": 138}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since wordbreaks are not conventionally marked in Chinese text corpora , a character-based collocation system has the dual advantages of avoiding pre-processing distortion and directly accessing sub-lexical information .", "output": {"relations": {"feature of": [{"head": {"text": "avoiding pre-processing distortion", "start": 137, "end": 171}, "tail": {"text": "character-based collocation system", "start": 75, "end": 109}}, {"head": {"text": "accessing sub-lexical information", "start": 185, "end": 218}, "tail": {"text": "character-based collocation system", "start": 75, "end": 109}}], "conjunction": [{"head": {"text": "avoiding pre-processing distortion", "start": 137, "end": 171}, "tail": {"text": "accessing sub-lexical information", "start": 185, "end": 218}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , word-based collocational properties can be obtained through an auxiliary module of automatic segmentation .", "output": {"relations": {"used for": [{"head": {"text": "auxiliary module of automatic segmentation", "start": 77, "end": 119}, "tail": {"text": "word-based collocational properties", "start": 14, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a method for utterance classification that does not require manual transcription of training data .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 23, "end": 29}, "tail": {"text": "utterance classification", "start": 34, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "utterance classification", "start": 94, "end": 118}}, {"head": {"text": "word-trigram recognition", "start": 201, "end": 225}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "manual transcription", "start": 236, "end": 256}, "tail": {"text": "word-trigram recognition", "start": 201, "end": 225}}], "part of": [{"head": {"text": "domain independent acoustic models", "start": 20, "end": 54}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "classifiers", "start": 74, "end": 85}, "tail": {"text": "method", "start": 4, "end": 10}}], "conjunction": [{"head": {"text": "classifiers", "start": 74, "end": 85}, "tail": {"text": "domain independent acoustic models", "start": 20, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our method , unsupervised training is first used to train a phone n-gram model for a particular domain ; the output of recognition with this model is then passed to a phone-string classifier .", "output": {"relations": {"part of": [{"head": {"text": "unsupervised training", "start": 16, "end": 37}, "tail": {"text": "method", "start": 7, "end": 13}}], "used for": [{"head": {"text": "unsupervised training", "start": 16, "end": 37}, "tail": {"text": "phone n-gram model", "start": 63, "end": 81}}, {"head": {"text": "unsupervised training", "start": 16, "end": 37}, "tail": {"text": "domain", "start": 99, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The classification accuracy of the method is evaluated on three different spoken language system domains .", "output": {"relations": {"evaluate for": [{"head": {"text": "classification accuracy", "start": 4, "end": 27}, "tail": {"text": "method", "start": 35, "end": 41}}, {"head": {"text": "spoken language system domains", "start": 74, "end": 104}, "tail": {"text": "method", "start": 35, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Interval Algebra -LRB- IA -RRB- and a subset of the Region Connection Calculus -LRB- RCC -RRB- , namely RCC-8 , are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively .", "output": {"relations": {"conjunction": [{"head": {"text": "Interval Algebra -LRB- IA -RRB-", "start": 4, "end": 35}, "tail": {"text": "Region Connection Calculus -LRB- RCC -RRB-", "start": 56, "end": 98}}], "hyponym of": [{"head": {"text": "Interval Algebra -LRB- IA -RRB-", "start": 4, "end": 35}, "tail": {"text": "Artificial Intelligence approaches", "start": 133, "end": 167}}, {"head": {"text": "Region Connection Calculus -LRB- RCC -RRB-", "start": 56, "end": 98}, "tail": {"text": "Artificial Intelligence approaches", "start": 133, "end": 167}}, {"head": {"text": "RCC-8", "start": 108, "end": 113}, "tail": {"text": "Region Connection Calculus -LRB- RCC -RRB-", "start": 56, "end": 98}}], "used for": [{"head": {"text": "Artificial Intelligence approaches", "start": 133, "end": 167}, "tail": {"text": "representing and reasoning about qualitative temporal and topological relations", "start": 172, "end": 251}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such qualitative information can be formulated as a Qualitative Constraint Network -LRB- QCN -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "Qualitative Constraint Network -LRB- QCN -RRB-", "start": 52, "end": 98}, "tail": {"text": "qualitative information", "start": 5, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we focus on the minimal labeling problem -LRB- MLP -RRB- and we propose an algorithm to efficiently derive all the feasible base relations of a QCN .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 91, "end": 100}, "tail": {"text": "QCN", "start": 160, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our algorithm considers chordal QCNs and a new form of partial consistency which we define as ◆ G-consistency .", "output": {"relations": {"part of": [{"head": {"text": "chordal QCNs", "start": 24, "end": 36}, "tail": {"text": "algorithm", "start": 4, "end": 13}}, {"head": {"text": "partial consistency", "start": 55, "end": 74}, "tail": {"text": "algorithm", "start": 4, "end": 13}}], "conjunction": [{"head": {"text": "chordal QCNs", "start": 24, "end": 36}, "tail": {"text": "partial consistency", "start": 55, "end": 74}}], "hyponym of": [{"head": {"text": "◆ G-consistency", "start": 94, "end": 109}, "tail": {"text": "partial consistency", "start": 55, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experi-mentations with QCNs of IA and RCC-8 show the importance and efficiency of this new approach .", "output": {"relations": {"evaluate for": [{"head": {"text": "QCNs of IA and RCC-8", "start": 23, "end": 43}, "tail": {"text": "approach", "start": 91, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper a morphological component with a limited capability to automatically interpret -LRB- and generate -RRB- derived words is presented .", "output": {"relations": {"used for": [{"head": {"text": "morphological component", "start": 16, "end": 39}, "tail": {"text": "derived words", "start": 118, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system combines an extended two-level morphology -LSB- Trost , 1991a ; Trost , 1991b -RSB- with a feature-based word grammar building on a hierarchical lexicon .", "output": {"relations": {"used for": [{"head": {"text": "two-level morphology", "start": 32, "end": 52}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "hierarchical lexicon", "start": 143, "end": 163}, "tail": {"text": "feature-based word grammar", "start": 102, "end": 128}}], "conjunction": [{"head": {"text": "two-level morphology", "start": 32, "end": 52}, "tail": {"text": "feature-based word grammar", "start": 102, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Polymorphemic stems not explicitly stored in the lexicon are given a compositional interpretation .", "output": {"relations": {"feature of": [{"head": {"text": "compositional interpretation", "start": 69, "end": 97}, "tail": {"text": "Polymorphemic stems", "start": 0, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system is implemented in CommonLisp and has been tested on examples from German derivation .", "output": {"relations": {"used for": [{"head": {"text": "CommonLisp", "start": 29, "end": 39}, "tail": {"text": "system", "start": 4, "end": 10}}], "evaluate for": [{"head": {"text": "German derivation", "start": 77, "end": 94}, "tail": {"text": "system", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Four problems render vector space model -LRB- VSM -RRB- - based text classification approach ineffective : 1 -RRB- Many words within song lyrics actually contribute little to sentiment ; 2 -RRB- Nouns and verbs used to express sentiment are ambiguous ; 3 -RRB- Negations and modifiers around the sentiment keywords make particular contributions to sentiment ; 4 -RRB- Song lyric is usually very short .", "output": {"relations": {"conjunction": [{"head": {"text": "Negations", "start": 261, "end": 270}, "tail": {"text": "modifiers", "start": 275, "end": 284}}], "used for": [{"head": {"text": "Negations", "start": 261, "end": 270}, "tail": {"text": "sentiment", "start": 175, "end": 184}}, {"head": {"text": "modifiers", "start": 275, "end": 284}, "tail": {"text": "sentiment", "start": 175, "end": 184}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To address these problems , the sentiment vector space model -LRB- s-VSM -RRB- is proposed to represent song lyric document .", "output": {"relations": {"used for": [{"head": {"text": "sentiment vector space model -LRB- s-VSM -RRB-", "start": 32, "end": 78}, "tail": {"text": "song lyric document", "start": 104, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The preliminary experiments prove that the s-VSM model outperforms the VSM model in the lyric-based song sentiment classification task .", "output": {"relations": {"compare": [{"head": {"text": "s-VSM model", "start": 43, "end": 54}, "tail": {"text": "VSM model", "start": 45, "end": 54}}], "evaluate for": [{"head": {"text": "lyric-based song sentiment classification task", "start": 88, "end": 134}, "tail": {"text": "s-VSM model", "start": 43, "end": 54}}, {"head": {"text": "lyric-based song sentiment classification task", "start": 88, "end": 134}, "tail": {"text": "VSM model", "start": 45, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an efficient algorithm for the redundancy elimination problem : Given an underspecified semantic representation -LRB- USR -RRB- of a scope ambiguity , compute an USR with fewer mutually equivalent readings .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 24, "end": 33}, "tail": {"text": "redundancy elimination problem", "start": 42, "end": 72}}, {"head": {"text": "underspecified semantic representation -LRB- USR -RRB-", "start": 84, "end": 138}, "tail": {"text": "scope ambiguity", "start": 144, "end": 159}}, {"head": {"text": "equivalent readings", "start": 197, "end": 216}, "tail": {"text": "USR", "start": 129, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The algorithm operates on underspecified chart representations which are derived from dominance graphs ; it can be applied to the USRs computed by large-scale grammars .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 4, "end": 13}, "tail": {"text": "underspecified chart representations", "start": 26, "end": 62}}, {"head": {"text": "dominance graphs", "start": 86, "end": 102}, "tail": {"text": "underspecified chart representations", "start": 26, "end": 62}}, {"head": {"text": "it", "start": 9, "end": 11}, "tail": {"text": "USRs", "start": 130, "end": 134}}, {"head": {"text": "large-scale grammars", "start": 147, "end": 167}, "tail": {"text": "USRs", "start": 130, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the algorithm on a corpus , and show that it reduces the degree of ambiguity significantly while taking negligible runtime .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 21, "end": 23}, "tail": {"text": "degree of ambiguity", "start": 69, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Currently several grammatical formalisms converge towards being declarative and towards utilizing context-free phrase-structure grammar as a backbone , e.g. LFG and PATR-II .", "output": {"relations": {"used for": [{"head": {"text": "context-free phrase-structure grammar", "start": 98, "end": 135}, "tail": {"text": "grammatical formalisms", "start": 18, "end": 40}}], "hyponym of": [{"head": {"text": "LFG", "start": 157, "end": 160}, "tail": {"text": "grammatical formalisms", "start": 18, "end": 40}}, {"head": {"text": "PATR-II", "start": 165, "end": 172}, "tail": {"text": "grammatical formalisms", "start": 18, "end": 40}}], "conjunction": [{"head": {"text": "LFG", "start": 157, "end": 160}, "tail": {"text": "PATR-II", "start": 165, "end": 172}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Typically the processing of these formalisms is organized within a chart-parsing framework .", "output": {"relations": {"feature of": [{"head": {"text": "chart-parsing framework", "start": 67, "end": 90}, "tail": {"text": "formalisms", "start": 34, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The aim of this paper is to provide a survey and a practical comparison of fundamental rule-invocation strategies within context-free chart parsing .", "output": {"relations": {"part of": [{"head": {"text": "rule-invocation strategies", "start": 87, "end": 113}, "tail": {"text": "context-free chart parsing", "start": 121, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The present paper focusses on terminology structuring by lexical methods , which match terms on the basis on their content words , taking morphological variants into account .", "output": {"relations": {"used for": [{"head": {"text": "lexical methods", "start": 57, "end": 72}, "tail": {"text": "terminology structuring", "start": 30, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments are done on a ` flat ' list of terms obtained from an originally hierarchically-structured terminology : the French version of the US National Library of Medicine MeSH thesaurus .", "output": {"relations": {"hyponym of": [{"head": {"text": "US National Library of Medicine MeSH thesaurus", "start": 143, "end": 189}, "tail": {"text": "hierarchically-structured terminology", "start": 77, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compare the lexically-induced relations with the original MeSH relations : after a quantitative evaluation of their congruence through recall and precision metrics , we perform a qualitative , human analysis ofthe ` new ' relations not present in the MeSH .", "output": {"relations": {"compare": [{"head": {"text": "lexically-induced relations", "start": 15, "end": 42}, "tail": {"text": "MeSH relations", "start": 61, "end": 75}}], "evaluate for": [{"head": {"text": "recall and precision metrics", "start": 138, "end": 166}, "tail": {"text": "MeSH relations", "start": 61, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In order to boost the translation quality of EBMT based on a small-sized bilingual corpus , we use an out-of-domain bilingual corpus and , in addition , the language model of an in-domain monolingual corpus .", "output": {"relations": {"used for": [{"head": {"text": "small-sized bilingual corpus", "start": 61, "end": 89}, "tail": {"text": "EBMT", "start": 45, "end": 49}}, {"head": {"text": "out-of-domain bilingual corpus", "start": 102, "end": 132}, "tail": {"text": "EBMT", "start": 45, "end": 49}}, {"head": {"text": "language model", "start": 157, "end": 171}, "tail": {"text": "EBMT", "start": 45, "end": 49}}, {"head": {"text": "in-domain monolingual corpus", "start": 178, "end": 206}, "tail": {"text": "language model", "start": 157, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model .", "output": {"relations": {"used for": [{"head": {"text": "evaluation measures", "start": 8, "end": 27}, "tail": {"text": "out-of-domain bilingual corpus", "start": 101, "end": 131}}], "evaluate for": [{"head": {"text": "evaluation measures", "start": 8, "end": 27}, "tail": {"text": "language model", "start": 165, "end": 179}}], "hyponym of": [{"head": {"text": "BLEU score", "start": 35, "end": 45}, "tail": {"text": "evaluation measures", "start": 8, "end": 27}}, {"head": {"text": "NIST score", "start": 54, "end": 64}, "tail": {"text": "evaluation measures", "start": 8, "end": 27}}], "conjunction": [{"head": {"text": "BLEU score", "start": 35, "end": 45}, "tail": {"text": "NIST score", "start": 54, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Diagrams are common tools for representing complex concepts , relationships and events , often when it would be difficult to portray the same information with natural images .", "output": {"relations": {"used for": [{"head": {"text": "Diagrams", "start": 0, "end": 8}, "tail": {"text": "complex concepts", "start": 43, "end": 59}}, {"head": {"text": "Diagrams", "start": 0, "end": 8}, "tail": {"text": "relationships", "start": 62, "end": 75}}, {"head": {"text": "Diagrams", "start": 0, "end": 8}, "tail": {"text": "events", "start": 80, "end": 86}}], "conjunction": [{"head": {"text": "complex concepts", "start": 43, "end": 59}, "tail": {"text": "relationships", "start": 62, "end": 75}}, {"head": {"text": "relationships", "start": 62, "end": 75}, "tail": {"text": "events", "start": 80, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Understanding natural images has been extensively studied in computer vision , while diagram understanding has received little attention .", "output": {"relations": {"part of": [{"head": {"text": "Understanding natural images", "start": 0, "end": 28}, "tail": {"text": "computer vision", "start": 61, "end": 76}}], "compare": [{"head": {"text": "Understanding natural images", "start": 0, "end": 28}, "tail": {"text": "diagram understanding", "start": 85, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we study the problem of diagram interpretation and reasoning , the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships .", "output": {"relations": {"used for": [{"head": {"text": "task", "start": 95, "end": 99}, "tail": {"text": "structure of a diagram", "start": 119, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce Diagram Parse Graphs -LRB- DPG -RRB- as our representation to model the structure of diagrams .", "output": {"relations": {"used for": [{"head": {"text": "Diagram Parse Graphs -LRB- DPG -RRB-", "start": 13, "end": 49}, "tail": {"text": "structure of diagrams", "start": 85, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering .", "output": {"relations": {"used for": [{"head": {"text": "syntactic parsing of diagrams", "start": 10, "end": 39}, "tail": {"text": "DPGs", "start": 61, "end": 65}}, {"head": {"text": "semantic interpretation and reasoning of diagrams", "start": 89, "end": 138}, "tail": {"text": "diagram question answering", "start": 157, "end": 183}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering .", "output": {"relations": {"used for": [{"head": {"text": "LSTM-based method", "start": 13, "end": 30}, "tail": {"text": "syntactic parsing of diagrams", "start": 35, "end": 64}}, {"head": {"text": "DPG-based attention model", "start": 81, "end": 106}, "tail": {"text": "diagram question answering", "start": 111, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers .", "output": {"relations": {"feature of": [{"head": {"text": "diagrams", "start": 28, "end": 36}, "tail": {"text": "dataset", "start": 17, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs .", "output": {"relations": {"used for": [{"head": {"text": "models", "start": 41, "end": 47}, "tail": {"text": "syntactic parsing and question answering in diagrams", "start": 52, "end": 104}}, {"head": {"text": "DPGs", "start": 111, "end": 115}, "tail": {"text": "models", "start": 41, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous change detection methods , focusing on detecting large-scale significant changes , can not do this well .", "output": {"relations": {"used for": [{"head": {"text": "change detection methods", "start": 9, "end": 33}, "tail": {"text": "detecting large-scale significant changes", "start": 48, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a feasible end-to-end approach to this challenging problem .", "output": {"relations": {"used for": [{"head": {"text": "end-to-end approach", "start": 31, "end": 50}, "tail": {"text": "problem", "start": 71, "end": 78}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Given two times observations , we formulate fine-grained change detection as a joint optimization problem of three related factors , i.e. , normal-aware lighting difference , camera geometry correction flow , and real scene change mask .", "output": {"relations": {"used for": [{"head": {"text": "joint optimization problem", "start": 79, "end": 105}, "tail": {"text": "fine-grained change detection", "start": 44, "end": 73}}], "feature of": [{"head": {"text": "factors", "start": 123, "end": 130}, "tail": {"text": "joint optimization problem", "start": 79, "end": 105}}], "hyponym of": [{"head": {"text": "normal-aware lighting difference", "start": 140, "end": 172}, "tail": {"text": "factors", "start": 123, "end": 130}}, {"head": {"text": "camera geometry correction flow", "start": 175, "end": 206}, "tail": {"text": "factors", "start": 123, "end": 130}}, {"head": {"text": "real scene change mask", "start": 213, "end": 235}, "tail": {"text": "factors", "start": 123, "end": 130}}], "conjunction": [{"head": {"text": "normal-aware lighting difference", "start": 140, "end": 172}, "tail": {"text": "camera geometry correction flow", "start": 175, "end": 206}}, {"head": {"text": "camera geometry correction flow", "start": 175, "end": 206}, "tail": {"text": "real scene change mask", "start": 213, "end": 235}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We solve the three factors in a coarse-to-fine manner and achieve reliable change decision by rank minimization .", "output": {"relations": {"used for": [{"head": {"text": "coarse-to-fine manner", "start": 32, "end": 53}, "tail": {"text": "factors", "start": 19, "end": 26}}, {"head": {"text": "rank minimization", "start": 94, "end": 111}, "tail": {"text": "change decision", "start": 75, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We build three real-world datasets to benchmark fine-grained change detection of misaligned scenes under varied multiple lighting conditions .", "output": {"relations": {"evaluate for": [{"head": {"text": "real-world datasets", "start": 15, "end": 34}, "tail": {"text": "fine-grained change detection of misaligned scenes", "start": 48, "end": 98}}], "feature of": [{"head": {"text": "varied multiple lighting conditions", "start": 105, "end": 140}, "tail": {"text": "fine-grained change detection of misaligned scenes", "start": 48, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments show the superior performance of our approach over state-of-the-art change detection methods and its ability to distinguish real scene changes from false ones caused by lighting variations .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 59, "end": 67}, "tail": {"text": "change detection methods", "start": 90, "end": 114}}], "used for": [{"head": {"text": "approach", "start": 59, "end": 67}, "tail": {"text": "real scene changes", "start": 146, "end": 164}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Automatic evaluation metrics for Machine Translation -LRB- MT -RRB- systems , such as BLEU or NIST , are now well established .", "output": {"relations": {"evaluate for": [{"head": {"text": "Automatic evaluation metrics", "start": 0, "end": 28}, "tail": {"text": "Machine Translation -LRB- MT -RRB- systems", "start": 33, "end": 75}}], "hyponym of": [{"head": {"text": "BLEU", "start": 86, "end": 90}, "tail": {"text": "Automatic evaluation metrics", "start": 0, "end": 28}}, {"head": {"text": "NIST", "start": 94, "end": 98}, "tail": {"text": "Automatic evaluation metrics", "start": 0, "end": 28}}], "conjunction": [{"head": {"text": "BLEU", "start": 86, "end": 90}, "tail": {"text": "NIST", "start": 94, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Yet , they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese , because of the word segmentation problem .", "output": {"relations": {"used for": [{"head": {"text": "they", "start": 6, "end": 10}, "tail": {"text": "assessment of language pairs", "start": 37, "end": 65}}], "hyponym of": [{"head": {"text": "English-Chinese", "start": 71, "end": 86}, "tail": {"text": "language pairs", "start": 51, "end": 65}}, {"head": {"text": "English-Japanese", "start": 90, "end": 106}, "tail": {"text": "language pairs", "start": 51, "end": 65}}], "conjunction": [{"head": {"text": "English-Chinese", "start": 71, "end": 86}, "tail": {"text": "English-Japanese", "start": 90, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level .", "output": {"relations": {"used for": [{"head": {"text": "BLEU", "start": 67, "end": 71}, "tail": {"text": "word n-grams", "start": 75, "end": 87}}, {"head": {"text": "BLEU", "start": 67, "end": 71}, "tail": {"text": "character level", "start": 115, "end": 130}}], "conjunction": [{"head": {"text": "word n-grams", "start": 75, "end": 87}, "tail": {"text": "character level", "start": 115, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The use of BLEU at the character level eliminates the word segmentation problem : it makes it possible to directly compare commercial systems outputting unsegmented texts with , for instance , statistical MT systems which usually segment their outputs .", "output": {"relations": {"used for": [{"head": {"text": "BLEU", "start": 11, "end": 15}, "tail": {"text": "character level", "start": 23, "end": 38}}, {"head": {"text": "BLEU", "start": 11, "end": 15}, "tail": {"text": "word segmentation problem", "start": 54, "end": 79}}], "evaluate for": [{"head": {"text": "it", "start": 82, "end": 84}, "tail": {"text": "commercial systems", "start": 123, "end": 141}}, {"head": {"text": "it", "start": 82, "end": 84}, "tail": {"text": "statistical MT systems", "start": 193, "end": 215}}], "compare": [{"head": {"text": "commercial systems", "start": 123, "end": 141}, "tail": {"text": "statistical MT systems", "start": 193, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a series of modifications to the left corner parsing algorithm for context-free grammars .", "output": {"relations": {"used for": [{"head": {"text": "left corner parsing algorithm", "start": 53, "end": 82}, "tail": {"text": "context-free grammars", "start": 87, "end": 108}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is argued that the resulting algorithm is both efficient and flexible and is , therefore , a good choice for the parser used in a natural language interface .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 32, "end": 41}, "tail": {"text": "parser", "start": 116, "end": 122}}, {"head": {"text": "parser", "start": 116, "end": 122}, "tail": {"text": "natural language interface", "start": 133, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a novel statistical singing voice conversion -LRB- SVC -RRB- technique with direct waveform modification based on the spectrum differential that can convert voice timbre of a source singer into that of a target singer without using a vocoder to generate converted singing voice waveforms .", "output": {"relations": {"used for": [{"head": {"text": "direct waveform modification", "start": 96, "end": 124}, "tail": {"text": "statistical singing voice conversion -LRB- SVC -RRB- technique", "start": 28, "end": 90}}, {"head": {"text": "spectrum differential", "start": 138, "end": 159}, "tail": {"text": "direct waveform modification", "start": 96, "end": 124}}, {"head": {"text": "spectrum differential", "start": 138, "end": 159}, "tail": {"text": "voice timbre", "start": 177, "end": 189}}, {"head": {"text": "vocoder", "start": 254, "end": 261}, "tail": {"text": "converted singing voice waveforms", "start": 274, "end": 307}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "SVC makes it possible to convert singing voice characteristics of an arbitrary source singer into those of an arbitrary target singer .", "output": {"relations": {"used for": [{"head": {"text": "SVC", "start": 0, "end": 3}, "tail": {"text": "singing voice characteristics", "start": 33, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , speech quality of the converted singing voice is significantly degraded compared to that of a natural singing voice due to various factors , such as analysis and modeling errors in the vocoder-based framework .", "output": {"relations": {"evaluate for": [{"head": {"text": "speech quality", "start": 10, "end": 24}, "tail": {"text": "converted singing voice", "start": 32, "end": 55}}, {"head": {"text": "speech quality", "start": 10, "end": 24}, "tail": {"text": "natural singing voice", "start": 104, "end": 125}}], "compare": [{"head": {"text": "converted singing voice", "start": 32, "end": 55}, "tail": {"text": "natural singing voice", "start": 104, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The differential spectral feature is directly estimated using a differential Gaussian mixture model -LRB- GMM -RRB- that is analytically derived from the traditional GMM used as a conversion model in the conventional SVC .", "output": {"relations": {"used for": [{"head": {"text": "differential Gaussian mixture model -LRB- GMM -RRB-", "start": 64, "end": 115}, "tail": {"text": "differential spectral feature", "start": 4, "end": 33}}, {"head": {"text": "GMM", "start": 106, "end": 109}, "tail": {"text": "differential Gaussian mixture model -LRB- GMM -RRB-", "start": 64, "end": 115}}, {"head": {"text": "GMM", "start": 106, "end": 109}, "tail": {"text": "conversion model", "start": 180, "end": 196}}, {"head": {"text": "conversion model", "start": 180, "end": 196}, "tail": {"text": "SVC", "start": 217, "end": 220}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results demonstrate that the proposed method makes it possible to significantly improve speech quality in the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional SVC .", "output": {"relations": {"compare": [{"head": {"text": "method", "start": 55, "end": 61}, "tail": {"text": "SVC", "start": 240, "end": 243}}], "evaluate for": [{"head": {"text": "speech quality", "start": 105, "end": 119}, "tail": {"text": "method", "start": 55, "end": 61}}, {"head": {"text": "speech quality", "start": 105, "end": 119}, "tail": {"text": "SVC", "start": 240, "end": 243}}, {"head": {"text": "conversion accuracy of singer identity", "start": 172, "end": 210}, "tail": {"text": "method", "start": 55, "end": 61}}], "used for": [{"head": {"text": "conversion accuracy of singer identity", "start": 172, "end": 210}, "tail": {"text": "SVC", "start": 240, "end": 243}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "During late-2013 through early-2014 NIST coordinated a special i-vector challenge based on data used in previous NIST Speaker Recognition Evaluations -LRB- SREs -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "NIST Speaker Recognition Evaluations -LRB- SREs -RRB-", "start": 113, "end": 166}, "tail": {"text": "i-vector challenge", "start": 63, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unlike evaluations in the SRE series , the i-vector challenge was run entirely online and used fixed-length feature vectors projected into a low-dimensional space -LRB- i-vectors -RRB- rather than audio recordings .", "output": {"relations": {"used for": [{"head": {"text": "fixed-length feature vectors", "start": 95, "end": 123}, "tail": {"text": "low-dimensional space -LRB- i-vectors -RRB-", "start": 141, "end": 184}}, {"head": {"text": "low-dimensional space -LRB- i-vectors -RRB-", "start": 141, "end": 184}, "tail": {"text": "i-vector challenge", "start": 43, "end": 61}}], "compare": [{"head": {"text": "audio recordings", "start": 197, "end": 213}, "tail": {"text": "low-dimensional space -LRB- i-vectors -RRB-", "start": 141, "end": 184}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Compared to the 2012 SRE , the i-vector challenge saw an increase in the number of participants by nearly a factor of two , and a two orders of magnitude increase in the number of systems submitted for evaluation .", "output": {"relations": {"compare": [{"head": {"text": "SRE", "start": 21, "end": 24}, "tail": {"text": "i-vector challenge", "start": 31, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Initial results indicate the leading system achieved an approximate 37 % improvement relative to the baseline system .", "output": {"relations": {"compare": [{"head": {"text": "leading system", "start": 29, "end": 43}, "tail": {"text": "baseline system", "start": 101, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Theoretical research in the area of machine translation usually involves the search for and creation of an appropriate formalism .", "output": {"relations": {"used for": [{"head": {"text": "formalism", "start": 119, "end": 128}, "tail": {"text": "machine translation", "start": 36, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we will introduce the anaphoric component of the Mimo formalism .", "output": {"relations": {"part of": [{"head": {"text": "anaphoric component", "start": 38, "end": 57}, "tail": {"text": "Mimo formalism", "start": 65, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In Mimo , the translation of anaphoric relations is compositional .", "output": {"relations": {"used for": [{"head": {"text": "Mimo", "start": 3, "end": 7}, "tail": {"text": "translation of anaphoric relations", "start": 14, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The anaphoric component is used to define linguistic phenomena such as wh-movement , the passive and the binding of reflexives and pronouns mono-lingually .", "output": {"relations": {"used for": [{"head": {"text": "anaphoric component", "start": 4, "end": 23}, "tail": {"text": "linguistic phenomena", "start": 42, "end": 62}}], "hyponym of": [{"head": {"text": "wh-movement", "start": 71, "end": 82}, "tail": {"text": "linguistic phenomena", "start": 42, "end": 62}}, {"head": {"text": "the passive and the binding of reflexives and pronouns", "start": 85, "end": 139}, "tail": {"text": "linguistic phenomena", "start": 42, "end": 62}}], "conjunction": [{"head": {"text": "wh-movement", "start": 71, "end": 82}, "tail": {"text": "the passive and the binding of reflexives and pronouns", "start": 85, "end": 139}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD 's .", "output": {"relations": {"conjunction": [{"head": {"text": "efficiency", "start": 4, "end": 14}, "tail": {"text": "quality", "start": 19, "end": 26}}], "feature of": [{"head": {"text": "images of popular music CD 's", "start": 115, "end": 144}, "tail": {"text": "database", "start": 97, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The scheme builds upon popular techniques of indexing descriptors extracted from local regions , and is robust to background clutter and occlusion .", "output": {"relations": {"used for": [{"head": {"text": "scheme", "start": 4, "end": 10}, "tail": {"text": "background clutter", "start": 114, "end": 132}}, {"head": {"text": "scheme", "start": 4, "end": 10}, "tail": {"text": "occlusion", "start": 137, "end": 146}}, {"head": {"text": "indexing descriptors", "start": 45, "end": 65}, "tail": {"text": "scheme", "start": 4, "end": 10}}, {"head": {"text": "local regions", "start": 81, "end": 94}, "tail": {"text": "indexing descriptors", "start": 45, "end": 65}}], "conjunction": [{"head": {"text": "background clutter", "start": 114, "end": 132}, "tail": {"text": "occlusion", "start": 137, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The local region descriptors are hierarchically quantized in a vocabulary tree .", "output": {"relations": {"used for": [{"head": {"text": "vocabulary tree", "start": 63, "end": 78}, "tail": {"text": "local region descriptors", "start": 4, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The quantization and the indexing are therefore fully integrated , essentially being one and the same .", "output": {"relations": {"conjunction": [{"head": {"text": "quantization", "start": 4, "end": 16}, "tail": {"text": "indexing", "start": 25, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The recognition quality is evaluated through retrieval on a database with ground truth , showing the power of the vocabulary tree approach , going as high as 1 million images .", "output": {"relations": {"evaluate for": [{"head": {"text": "recognition quality", "start": 4, "end": 23}, "tail": {"text": "vocabulary tree approach", "start": 114, "end": 138}}, {"head": {"text": "retrieval", "start": 45, "end": 54}, "tail": {"text": "recognition quality", "start": 4, "end": 23}}], "used for": [{"head": {"text": "database with ground truth", "start": 60, "end": 86}, "tail": {"text": "retrieval", "start": 45, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a method for blind estimation of reverberation times in reverberant enclosures .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "blind estimation of reverberation times", "start": 33, "end": 72}}], "feature of": [{"head": {"text": "reverberant enclosures", "start": 76, "end": 98}, "tail": {"text": "blind estimation of reverberation times", "start": 33, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed algorithm is based on a statistical model of short-term log-energy sequences for echo-free speech .", "output": {"relations": {"used for": [{"head": {"text": "statistical model of short-term log-energy sequences", "start": 37, "end": 89}, "tail": {"text": "algorithm", "start": 13, "end": 22}}, {"head": {"text": "statistical model of short-term log-energy sequences", "start": 37, "end": 89}, "tail": {"text": "echo-free speech", "start": 94, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method has been successfully applied to robust automatic speech recognition in reverberant environments by model selection .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "robust automatic speech recognition", "start": 44, "end": 79}}, {"head": {"text": "model selection", "start": 111, "end": 126}, "tail": {"text": "method", "start": 4, "end": 10}}], "feature of": [{"head": {"text": "reverberant environments", "start": 83, "end": 107}, "tail": {"text": "robust automatic speech recognition", "start": 44, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For this application , the reverberation time is first estimated from the reverberated speech utterance to be recognized .", "output": {"relations": {"used for": [{"head": {"text": "reverberated speech utterance", "start": 74, "end": 103}, "tail": {"text": "reverberation time", "start": 27, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The estimation is then used to select the best acoustic model out of a library of models trained in various artificial re-verberant conditions .", "output": {"relations": {"used for": [{"head": {"text": "estimation", "start": 4, "end": 14}, "tail": {"text": "acoustic model", "start": 47, "end": 61}}], "part of": [{"head": {"text": "acoustic model", "start": 47, "end": 61}, "tail": {"text": "models", "start": 82, "end": 88}}], "feature of": [{"head": {"text": "artificial re-verberant conditions", "start": 108, "end": 142}, "tail": {"text": "models", "start": 82, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Speech recognition experiments in simulated and real reverberant environments show the efficiency of our approach which outperforms standard channel normaliza-tion techniques .", "output": {"relations": {"evaluate for": [{"head": {"text": "Speech recognition", "start": 0, "end": 18}, "tail": {"text": "approach", "start": 105, "end": 113}}, {"head": {"text": "Speech recognition", "start": 0, "end": 18}, "tail": {"text": "channel normaliza-tion techniques", "start": 141, "end": 174}}], "feature of": [{"head": {"text": "simulated and real reverberant environments", "start": 34, "end": 77}, "tail": {"text": "Speech recognition", "start": 0, "end": 18}}], "compare": [{"head": {"text": "channel normaliza-tion techniques", "start": 141, "end": 174}, "tail": {"text": "approach", "start": 105, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For one thing , learning methodology applicable in general domains does not readily lend itself in the linguistic domain .", "output": {"relations": {"used for": [{"head": {"text": "learning methodology", "start": 16, "end": 36}, "tail": {"text": "general domains", "start": 51, "end": 66}}], "compare": [{"head": {"text": "general domains", "start": 51, "end": 66}, "tail": {"text": "linguistic domain", "start": 103, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For another , linguistic representation used by language processing systems is not geared to learning .", "output": {"relations": {"used for": [{"head": {"text": "linguistic representation", "start": 14, "end": 39}, "tail": {"text": "language processing systems", "start": 48, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduced a new linguistic representation , the Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB- -LSB- Zernik88 -RSB- , to facilitate language acquisition .", "output": {"relations": {"used for": [{"head": {"text": "linguistic representation", "start": 20, "end": 45}, "tail": {"text": "language acquisition", "start": 143, "end": 163}}, {"head": {"text": "Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB-", "start": 52, "end": 105}, "tail": {"text": "language acquisition", "start": 143, "end": 163}}], "hyponym of": [{"head": {"text": "Dynamic Hierarchical Phrasal Lexicon -LRB- DHPL -RRB-", "start": 52, "end": 105}, "tail": {"text": "linguistic representation", "start": 20, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "From this , a language learning model was implemented in the program RINA , which enhances its own lexical hierarchy by processing examples in context .", "output": {"relations": {"part of": [{"head": {"text": "language learning model", "start": 14, "end": 37}, "tail": {"text": "RINA", "start": 69, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We identified two tasks : First , how linguistic concepts are acquired from training examples and organized in a hierarchy ; this task was discussed in previous papers -LSB- Zernik87 -RSB- .", "output": {"relations": {"part of": [{"head": {"text": "linguistic concepts", "start": 38, "end": 57}, "tail": {"text": "hierarchy", "start": 113, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , we show in this paper how a lexical hierarchy is used in predicting new linguistic concepts .", "output": {"relations": {"used for": [{"head": {"text": "lexical hierarchy", "start": 37, "end": 54}, "tail": {"text": "linguistic concepts", "start": 81, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a novel ensemble learning approach to resolving German pronouns .", "output": {"relations": {"used for": [{"head": {"text": "ensemble learning approach", "start": 28, "end": 54}, "tail": {"text": "German pronouns", "start": 68, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments show that this approach is superior to a single decision-tree classifier .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 27, "end": 35}, "tail": {"text": "decision-tree classifier", "start": 60, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process .", "output": {"relations": {"used for": [{"head": {"text": "standalone system", "start": 27, "end": 44}, "tail": {"text": "pronouns", "start": 59, "end": 67}}, {"head": {"text": "standalone system", "start": 27, "end": 44}, "tail": {"text": "unannotated text", "start": 71, "end": 87}}, {"head": {"text": "preprocessing modules", "start": 127, "end": 148}, "tail": {"text": "standalone system", "start": 27, "end": 44}}, {"head": {"text": "preprocessing modules", "start": 127, "end": 148}, "tail": {"text": "manual annotation process", "start": 165, "end": 190}}], "part of": [{"head": {"text": "pronouns", "start": 59, "end": 67}, "tail": {"text": "unannotated text", "start": 71, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although the system performs well within a limited textual domain , further research is needed to make it effective for open-domain question answering and text summarisation .", "output": {"relations": {"evaluate for": [{"head": {"text": "textual domain", "start": 51, "end": 65}, "tail": {"text": "system", "start": 13, "end": 19}}], "compare": [{"head": {"text": "textual domain", "start": 51, "end": 65}, "tail": {"text": "open-domain question answering", "start": 120, "end": 150}}], "used for": [{"head": {"text": "it", "start": 35, "end": 37}, "tail": {"text": "open-domain question answering", "start": 120, "end": 150}}, {"head": {"text": "it", "start": 35, "end": 37}, "tail": {"text": "text summarisation", "start": 155, "end": 173}}], "conjunction": [{"head": {"text": "open-domain question answering", "start": 120, "end": 150}, "tail": {"text": "text summarisation", "start": 155, "end": 173}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we compare the performance of a state-of-the-art statistical parser -LRB- Bikel , 2004 -RRB- in parsing written and spoken language and in generating sub-categorization cues from written and spoken language .", "output": {"relations": {"used for": [{"head": {"text": "statistical parser", "start": 65, "end": 83}, "tail": {"text": "parsing written and spoken language", "start": 112, "end": 147}}, {"head": {"text": "statistical parser", "start": 65, "end": 83}, "tail": {"text": "generating sub-categorization cues", "start": 155, "end": 189}}, {"head": {"text": "written and spoken language", "start": 120, "end": 147}, "tail": {"text": "generating sub-categorization cues", "start": 155, "end": 189}}], "conjunction": [{"head": {"text": "parsing written and spoken language", "start": 112, "end": 147}, "tail": {"text": "generating sub-categorization cues", "start": 155, "end": 189}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although Bikel 's parser achieves a higher accuracy for parsing written language , it achieves a higher accuracy when extracting subcategorization cues from spoken language .", "output": {"relations": {"used for": [{"head": {"text": "Bikel 's parser", "start": 9, "end": 24}, "tail": {"text": "parsing written language", "start": 56, "end": 80}}, {"head": {"text": "it", "start": 66, "end": 68}, "tail": {"text": "subcategorization cues", "start": 129, "end": 151}}], "evaluate for": [{"head": {"text": "accuracy", "start": 43, "end": 51}, "tail": {"text": "Bikel 's parser", "start": 9, "end": 24}}, {"head": {"text": "accuracy", "start": 43, "end": 51}, "tail": {"text": "it", "start": 66, "end": 68}}], "part of": [{"head": {"text": "subcategorization cues", "start": 129, "end": 151}, "tail": {"text": "spoken language", "start": 157, "end": 172}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments also show that current technology for extracting subcategorization frames initially designed for written texts works equally well for spoken language .", "output": {"relations": {"used for": [{"head": {"text": "technology", "start": 39, "end": 49}, "tail": {"text": "extracting subcategorization frames", "start": 54, "end": 89}}, {"head": {"text": "technology", "start": 39, "end": 49}, "tail": {"text": "spoken language", "start": 150, "end": 165}}, {"head": {"text": "extracting subcategorization frames", "start": 54, "end": 89}, "tail": {"text": "written texts", "start": 113, "end": 126}}], "compare": [{"head": {"text": "written texts", "start": 113, "end": 126}, "tail": {"text": "spoken language", "start": 150, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Additionally , we explore the utility of punctuation in helping parsing and extraction of subcategorization cues .", "output": {"relations": {"used for": [{"head": {"text": "punctuation", "start": 41, "end": 52}, "tail": {"text": "parsing", "start": 64, "end": 71}}, {"head": {"text": "punctuation", "start": 41, "end": 52}, "tail": {"text": "extraction of subcategorization cues", "start": 76, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments show that punctuation is of little help in parsing spoken language and extracting subcategorization cues from spoken language .", "output": {"relations": {"conjunction": [{"head": {"text": "parsing spoken language", "start": 59, "end": 82}, "tail": {"text": "extracting subcategorization cues", "start": 87, "end": 120}}], "part of": [{"head": {"text": "subcategorization cues", "start": 98, "end": 120}, "tail": {"text": "spoken language", "start": 67, "end": 82}}], "used for": [{"head": {"text": "spoken language", "start": 67, "end": 82}, "tail": {"text": "extracting subcategorization cues", "start": 87, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes an alignment adaptation approach to improve domain-specific -LRB- in-domain -RRB- word alignment .", "output": {"relations": {"used for": [{"head": {"text": "alignment adaptation approach", "start": 23, "end": 52}, "tail": {"text": "domain-specific -LRB- in-domain -RRB- word alignment", "start": 64, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results .", "output": {"relations": {"used for": [{"head": {"text": "alignment adaptation", "start": 18, "end": 38}, "tail": {"text": "in-domain word alignment", "start": 81, "end": 105}}, {"head": {"text": "out-of-domain corpus", "start": 49, "end": 69}, "tail": {"text": "alignment adaptation", "start": 18, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively , and then interpolate these two models to improve the domain-specific word alignment .", "output": {"relations": {"used for": [{"head": {"text": "large-scale out-of-domain corpus", "start": 78, "end": 110}, "tail": {"text": "statistical word alignment models", "start": 35, "end": 68}}, {"head": {"text": "small-scale in-domain corpus", "start": 119, "end": 147}, "tail": {"text": "statistical word alignment models", "start": 35, "end": 68}}, {"head": {"text": "models", "start": 62, "end": 68}, "tail": {"text": "domain-specific word alignment", "start": 216, "end": 246}}], "conjunction": [{"head": {"text": "large-scale out-of-domain corpus", "start": 78, "end": 110}, "tail": {"text": "small-scale in-domain corpus", "start": 119, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall , achieving a relative error rate reduction of 6.56 % as compared with the state-of-the-art technologies .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 35, "end": 43}, "tail": {"text": "domain-specific word alignment", "start": 53, "end": 83}}], "compare": [{"head": {"text": "approach", "start": 35, "end": 43}, "tail": {"text": "state-of-the-art technologies", "start": 197, "end": 226}}], "evaluate for": [{"head": {"text": "precision", "start": 101, "end": 110}, "tail": {"text": "approach", "start": 35, "end": 43}}, {"head": {"text": "recall", "start": 115, "end": 121}, "tail": {"text": "approach", "start": 35, "end": 43}}, {"head": {"text": "relative error rate reduction", "start": 136, "end": 165}, "tail": {"text": "approach", "start": 35, "end": 43}}, {"head": {"text": "relative error rate reduction", "start": 136, "end": 165}, "tail": {"text": "state-of-the-art technologies", "start": 197, "end": 226}}], "conjunction": [{"head": {"text": "precision", "start": 101, "end": 110}, "tail": {"text": "recall", "start": 115, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With performance above 97 % accuracy for newspaper text , part of speech -LRB- pos -RRB- tagging might be considered a solved problem .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 28, "end": 36}, "tail": {"text": "part of speech -LRB- pos -RRB- tagging", "start": 58, "end": 96}}, {"head": {"text": "newspaper text", "start": 41, "end": 55}, "tail": {"text": "part of speech -LRB- pos -RRB- tagging", "start": 58, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous studies have shown that allowing the parser to resolve pos tag ambiguity does not improve performance .", "output": {"relations": {"used for": [{"head": {"text": "parser", "start": 46, "end": 52}, "tail": {"text": "pos tag ambiguity", "start": 64, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , for grammar formalisms which use more fine-grained grammatical categories , for example tag and ccg , tagging accuracy is much lower .", "output": {"relations": {"used for": [{"head": {"text": "fine-grained grammatical categories", "start": 48, "end": 83}, "tail": {"text": "grammar formalisms", "start": 14, "end": 32}}], "hyponym of": [{"head": {"text": "tag", "start": 112, "end": 115}, "tail": {"text": "fine-grained grammatical categories", "start": 48, "end": 83}}, {"head": {"text": "ccg", "start": 106, "end": 109}, "tail": {"text": "fine-grained grammatical categories", "start": 48, "end": 83}}], "evaluate for": [{"head": {"text": "tagging accuracy", "start": 112, "end": 128}, "tail": {"text": "grammar formalisms", "start": 14, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In fact , for these formalisms , premature ambiguity resolution makes parsing infeasible .", "output": {"relations": {"used for": [{"head": {"text": "parsing", "start": 70, "end": 77}, "tail": {"text": "formalisms", "start": 20, "end": 30}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a multi-tagging approach which maintains a suitable level of lexical category ambiguity for accurate and efficient ccg parsing .", "output": {"relations": {"used for": [{"head": {"text": "multi-tagging approach", "start": 14, "end": 36}, "tail": {"text": "ccg parsing", "start": 127, "end": 138}}], "feature of": [{"head": {"text": "lexical category ambiguity", "start": 73, "end": 99}, "tail": {"text": "multi-tagging approach", "start": 14, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We extend this multi-tagging approach to the pos level to overcome errors introduced by automatically assigned pos tags .", "output": {"relations": {"used for": [{"head": {"text": "multi-tagging approach", "start": 15, "end": 37}, "tail": {"text": "pos level", "start": 45, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although pos tagging accuracy seems high , maintaining some pos tag ambiguity in the language processing pipeline results in more accurate ccg supertagging .", "output": {"relations": {"feature of": [{"head": {"text": "pos tag ambiguity", "start": 60, "end": 77}, "tail": {"text": "language processing pipeline", "start": 85, "end": 113}}], "used for": [{"head": {"text": "pos tag ambiguity", "start": 60, "end": 77}, "tail": {"text": "ccg supertagging", "start": 139, "end": 155}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We previously presented a framework for segmentation of complex scenes using multiple physical hypotheses for simple image regions .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 26, "end": 35}, "tail": {"text": "segmentation of complex scenes", "start": 40, "end": 70}}, {"head": {"text": "physical hypotheses", "start": 86, "end": 105}, "tail": {"text": "framework", "start": 26, "end": 35}}, {"head": {"text": "physical hypotheses", "start": 86, "end": 105}, "tail": {"text": "simple image regions", "start": 110, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A consequence of that framework was a proposal for a new approach to the segmentation of complex scenes into regions corresponding to coherent surfaces rather than merely regions of similar color .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 22, "end": 31}, "tail": {"text": "approach", "start": 57, "end": 65}}, {"head": {"text": "approach", "start": 57, "end": 65}, "tail": {"text": "segmentation of complex scenes", "start": 73, "end": 103}}], "compare": [{"head": {"text": "coherent surfaces", "start": 134, "end": 151}, "tail": {"text": "regions of similar color", "start": 171, "end": 195}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Herein we present an implementation of this new approach and show example segmentations for scenes containing multi-colored piece-wise uniform objects .", "output": {"relations": {"used for": [{"head": {"text": "segmentations", "start": 74, "end": 87}, "tail": {"text": "scenes", "start": 92, "end": 98}}], "feature of": [{"head": {"text": "multi-colored piece-wise uniform objects", "start": 110, "end": 150}, "tail": {"text": "scenes", "start": 92, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using our approach we are able to intelligently segment scenes with objects of greater complexity than previous physics-based segmentation algorithms .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 10, "end": 18}, "tail": {"text": "physics-based segmentation algorithms", "start": 112, "end": 149}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "SmartKom is a multimodal dialog system that combines speech , gesture , and mimics input and output .", "output": {"relations": {"hyponym of": [{"head": {"text": "SmartKom", "start": 0, "end": 8}, "tail": {"text": "multimodal dialog system", "start": 14, "end": 38}}], "used for": [{"head": {"text": "speech", "start": 53, "end": 59}, "tail": {"text": "multimodal dialog system", "start": 14, "end": 38}}, {"head": {"text": "gesture", "start": 62, "end": 69}, "tail": {"text": "multimodal dialog system", "start": 14, "end": 38}}], "conjunction": [{"head": {"text": "speech", "start": 53, "end": 59}, "tail": {"text": "gesture", "start": 62, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Spontaneous speech understanding is combined with the video-based recognition of natural gestures .", "output": {"relations": {"conjunction": [{"head": {"text": "Spontaneous speech understanding", "start": 0, "end": 32}, "tail": {"text": "video-based recognition of natural gestures", "start": 54, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One of the major scientific goals of SmartKom is to design new computational methods for the seamless integration and mutual disambiguation of multimodal input and output on a semantic and pragmatic level .", "output": {"relations": {"used for": [{"head": {"text": "SmartKom", "start": 37, "end": 45}, "tail": {"text": "computational methods", "start": 63, "end": 84}}, {"head": {"text": "computational methods", "start": 63, "end": 84}, "tail": {"text": "integration and mutual disambiguation of multimodal input and output", "start": 102, "end": 170}}], "feature of": [{"head": {"text": "semantic and pragmatic level", "start": 176, "end": 204}, "tail": {"text": "integration and mutual disambiguation of multimodal input and output", "start": 102, "end": 170}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "SmartKom is based on the situated delegation-oriented dialog paradigm , in which the user delegates a task to a virtual communication assistant , visualized as a lifelike character on a graphical display .", "output": {"relations": {"used for": [{"head": {"text": "situated delegation-oriented dialog paradigm", "start": 25, "end": 69}, "tail": {"text": "SmartKom", "start": 0, "end": 8}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe the SmartKom architecture , the use of an XML-based markup language for multimodal content , and some of the distinguishing features of the first fully operational SmartKom demonstrator .", "output": {"relations": {"used for": [{"head": {"text": "XML-based markup language", "start": 54, "end": 79}, "tail": {"text": "multimodal content", "start": 84, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a single-image highlight removal method that incorporates illumination-based constraints into image in-painting .", "output": {"relations": {"used for": [{"head": {"text": "single-image highlight removal method", "start": 13, "end": 50}, "tail": {"text": "image in-painting", "start": 105, "end": 122}}], "part of": [{"head": {"text": "illumination-based constraints", "start": 69, "end": 99}, "tail": {"text": "image in-painting", "start": 105, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Constraints provided by observed pixel colors , highlight color analysis and illumination color uniformity are employed in our method to improve estimation of the underlying diffuse color .", "output": {"relations": {"used for": [{"head": {"text": "Constraints", "start": 0, "end": 11}, "tail": {"text": "method", "start": 127, "end": 133}}, {"head": {"text": "method", "start": 127, "end": 133}, "tail": {"text": "estimation of the underlying diffuse color", "start": 145, "end": 187}}], "conjunction": [{"head": {"text": "pixel colors", "start": 33, "end": 45}, "tail": {"text": "highlight color analysis", "start": 48, "end": 72}}, {"head": {"text": "highlight color analysis", "start": 48, "end": 72}, "tail": {"text": "illumination color uniformity", "start": 77, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The inclusion of these illumination constraints allows for better recovery of shading and textures by inpainting .", "output": {"relations": {"used for": [{"head": {"text": "illumination constraints", "start": 23, "end": 47}, "tail": {"text": "recovery of shading and textures", "start": 66, "end": 98}}, {"head": {"text": "inpainting", "start": 102, "end": 112}, "tail": {"text": "recovery of shading and textures", "start": 66, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a novel method , called local non-negative matrix factorization -LRB- LNMF -RRB- , for learning spatially localized , parts-based subspace representation of visual patterns .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 35, "end": 41}, "tail": {"text": "spatially localized , parts-based subspace representation of visual patterns", "start": 123, "end": 199}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An objective function is defined to impose lo-calization constraint , in addition to the non-negativity constraint in the standard NMF -LSB- 1 -RSB- .", "output": {"relations": {"used for": [{"head": {"text": "objective function", "start": 3, "end": 21}, "tail": {"text": "lo-calization constraint", "start": 43, "end": 67}}], "part of": [{"head": {"text": "non-negativity constraint", "start": 89, "end": 114}, "tail": {"text": "NMF", "start": 131, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An algorithm is presented for the learning of such basis components .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 3, "end": 12}, "tail": {"text": "learning", "start": 34, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition , which demonstrates advantages of LNMF .", "output": {"relations": {"compare": [{"head": {"text": "LNMF", "start": 46, "end": 50}, "tail": {"text": "NMF and PCA methods", "start": 60, "end": 79}}], "used for": [{"head": {"text": "LNMF", "start": 46, "end": 50}, "tail": {"text": "face representation and recognition", "start": 84, "end": 119}}, {"head": {"text": "NMF and PCA methods", "start": 60, "end": 79}, "tail": {"text": "face representation and recognition", "start": 84, "end": 119}}], "evaluate for": [{"head": {"text": "face representation and recognition", "start": 84, "end": 119}, "tail": {"text": "LNMF", "start": 46, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Many AI researchers have investigated useful ways of verifying and validating knowledge bases for ontologies and rules , but it is not easy to directly apply them to checking process models .", "output": {"relations": {"conjunction": [{"head": {"text": "ontologies", "start": 98, "end": 108}, "tail": {"text": "rules", "start": 113, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Other techniques developed for checking and refining planning knowledge tend to focus on automated plan generation rather than helping users author process information .", "output": {"relations": {"used for": [{"head": {"text": "checking and refining planning knowledge", "start": 31, "end": 71}, "tail": {"text": "automated plan generation", "start": 89, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a complementary approach which helps users author and check process models .", "output": {"relations": {"used for": [{"head": {"text": "complementary approach", "start": 29, "end": 51}, "tail": {"text": "process models", "start": 87, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It builds interdepen-dency models from this analysis and uses them to find errors and propose fixes .", "output": {"relations": {"used for": [{"head": {"text": "interdepen-dency models", "start": 10, "end": 33}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "them", "start": 62, "end": 66}, "tail": {"text": "errors", "start": 75, "end": 81}}, {"head": {"text": "them", "start": 62, "end": 66}, "tail": {"text": "fixes", "start": 94, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque .", "output": {"relations": {"used for": [{"head": {"text": "machine learning techniques", "start": 47, "end": 74}, "tail": {"text": "comma checker", "start": 86, "end": 99}}, {"head": {"text": "grammar checker", "start": 122, "end": 137}, "tail": {"text": "Basque", "start": 142, "end": 148}}], "part of": [{"head": {"text": "comma checker", "start": 86, "end": 99}, "tail": {"text": "grammar checker", "start": 122, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "After several experiments , and trained with a little corpus of 100,000 words , the system guesses correctly not placing commas with a precision of 96 % and a recall of 98 % .", "output": {"relations": {"evaluate for": [{"head": {"text": "precision", "start": 135, "end": 144}, "tail": {"text": "system", "start": 84, "end": 90}}, {"head": {"text": "recall", "start": 159, "end": 165}, "tail": {"text": "system", "start": 84, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It also gets a precision of 70 % and a recall of 49 % in the task of placing commas .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "placing commas", "start": 69, "end": 83}}], "evaluate for": [{"head": {"text": "precision", "start": 15, "end": 24}, "tail": {"text": "It", "start": 0, "end": 2}}, {"head": {"text": "recall", "start": 39, "end": 45}, "tail": {"text": "It", "start": 0, "end": 2}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The present paper reports on a preparatory research for building a language corpus annotation scenario capturing the discourse relations in Czech .", "output": {"relations": {"used for": [{"head": {"text": "language corpus annotation scenario", "start": 67, "end": 102}, "tail": {"text": "discourse relations", "start": 117, "end": 136}}], "feature of": [{"head": {"text": "Czech", "start": 140, "end": 145}, "tail": {"text": "discourse relations", "start": 117, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We primarily focus on the description of the syntactically motivated relations in discourse , basing our findings on the theoretical background of the Prague Dependency Treebank 2.0 and the Penn Discourse Treebank 2 .", "output": {"relations": {"used for": [{"head": {"text": "Prague Dependency Treebank 2.0", "start": 151, "end": 181}, "tail": {"text": "syntactically motivated relations in discourse", "start": 45, "end": 91}}, {"head": {"text": "Penn Discourse Treebank 2", "start": 190, "end": 215}, "tail": {"text": "syntactically motivated relations in discourse", "start": 45, "end": 91}}], "conjunction": [{"head": {"text": "Prague Dependency Treebank 2.0", "start": 151, "end": 181}, "tail": {"text": "Penn Discourse Treebank 2", "start": 190, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our aim is to revisit the present-day syntactico-semantic -LRB- tectogrammatical -RRB- annotation in the Prague Dependency Treebank , extend it for the purposes of a sentence-boundary-crossing representation and eventually to design a new , discourse level of annotation .", "output": {"relations": {"part of": [{"head": {"text": "syntactico-semantic -LRB- tectogrammatical -RRB- annotation", "start": 38, "end": 97}, "tail": {"text": "Prague Dependency Treebank", "start": 105, "end": 131}}], "used for": [{"head": {"text": "it", "start": 19, "end": 21}, "tail": {"text": "sentence-boundary-crossing representation", "start": 166, "end": 207}}, {"head": {"text": "it", "start": 19, "end": 21}, "tail": {"text": "discourse level of annotation", "start": 241, "end": 270}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a feasible process of such a transfer , comparing the possibilities the Praguian dependency-based approach offers with the Penn discourse annotation based primarily on the analysis and classification of discourse connectives .", "output": {"relations": {"compare": [{"head": {"text": "Penn discourse annotation", "start": 150, "end": 175}, "tail": {"text": "Praguian dependency-based approach", "start": 99, "end": 133}}], "evaluate for": [{"head": {"text": "analysis and classification of discourse connectives", "start": 199, "end": 251}, "tail": {"text": "Praguian dependency-based approach", "start": 99, "end": 133}}, {"head": {"text": "analysis and classification of discourse connectives", "start": 199, "end": 251}, "tail": {"text": "Penn discourse annotation", "start": 150, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Regression-based techniques have shown promising results for people counting in crowded scenes .", "output": {"relations": {"used for": [{"head": {"text": "Regression-based techniques", "start": 0, "end": 27}, "tail": {"text": "people counting in crowded scenes", "start": 61, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , most existing techniques require expensive and laborious data annotation for model training .", "output": {"relations": {"used for": [{"head": {"text": "data annotation", "start": 67, "end": 82}, "tail": {"text": "techniques", "start": 24, "end": 34}}, {"head": {"text": "data annotation", "start": 67, "end": 82}, "tail": {"text": "model training", "start": 87, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "-LRB- 2 -RRB- Rather than learning from only labelled data , the abundant unlabelled data are exploited .", "output": {"relations": {"compare": [{"head": {"text": "labelled data", "start": 45, "end": 58}, "tail": {"text": "abundant unlabelled data", "start": 65, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "All three ideas are implemented in a unified active and semi-supervised regression framework with ability to perform transfer learning , by exploiting the underlying geometric structure of crowd patterns via manifold analysis .", "output": {"relations": {"used for": [{"head": {"text": "unified active and semi-supervised regression framework", "start": 37, "end": 92}, "tail": {"text": "transfer learning", "start": 117, "end": 134}}, {"head": {"text": "geometric structure of crowd patterns", "start": 166, "end": 203}, "tail": {"text": "unified active and semi-supervised regression framework", "start": 37, "end": 92}}, {"head": {"text": "manifold analysis", "start": 208, "end": 225}, "tail": {"text": "geometric structure of crowd patterns", "start": 166, "end": 203}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Representing images with layers has many important applications , such as video compression , motion analysis , and 3D scene analysis .", "output": {"relations": {"used for": [{"head": {"text": "Representing images with layers", "start": 0, "end": 31}, "tail": {"text": "applications", "start": 51, "end": 63}}], "hyponym of": [{"head": {"text": "video compression", "start": 74, "end": 91}, "tail": {"text": "applications", "start": 51, "end": 63}}, {"head": {"text": "motion analysis", "start": 94, "end": 109}, "tail": {"text": "applications", "start": 51, "end": 63}}, {"head": {"text": "3D scene analysis", "start": 116, "end": 133}, "tail": {"text": "applications", "start": 51, "end": 63}}], "conjunction": [{"head": {"text": "video compression", "start": 74, "end": 91}, "tail": {"text": "motion analysis", "start": 94, "end": 109}}, {"head": {"text": "motion analysis", "start": 94, "end": 109}, "tail": {"text": "3D scene analysis", "start": 116, "end": 133}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an approach to reliably extracting layers from images by taking advantages of the fact that homographies induced by planar patches in the scene form a low dimensional linear subspace .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "layers", "start": 55, "end": 61}}], "part of": [{"head": {"text": "layers", "start": 55, "end": 61}, "tail": {"text": "images", "start": 67, "end": 73}}, {"head": {"text": "planar patches", "start": 136, "end": 150}, "tail": {"text": "scene", "start": 158, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Layers in the input images will be mapped in the subspace , where it is proven that they form well-defined clusters and can be reliably identified by a simple mean-shift based clustering algorithm .", "output": {"relations": {"part of": [{"head": {"text": "Layers", "start": 0, "end": 6}, "tail": {"text": "images", "start": 20, "end": 26}}], "used for": [{"head": {"text": "images", "start": 20, "end": 26}, "tail": {"text": "clusters", "start": 107, "end": 115}}, {"head": {"text": "mean-shift based clustering algorithm", "start": 159, "end": 196}, "tail": {"text": "clusters", "start": 107, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Global optimality is achieved since all valid regions are simultaneously taken into account , and noise can be effectively reduced by enforcing the subspace constraint .", "output": {"relations": {"used for": [{"head": {"text": "subspace constraint", "start": 148, "end": 167}, "tail": {"text": "noise", "start": 98, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The construction of causal graphs from non-experimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph .", "output": {"relations": {"used for": [{"head": {"text": "non-experimental data", "start": 39, "end": 60}, "tail": {"text": "construction of causal graphs", "start": 4, "end": 33}}], "feature of": [{"head": {"text": "probability distributions", "start": 131, "end": 156}, "tail": {"text": "graph", "start": 27, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These constraints are of two types : conditional inde-pendencies and algebraic constraints , first noted by Verma .", "output": {"relations": {"hyponym of": [{"head": {"text": "conditional inde-pendencies", "start": 37, "end": 64}, "tail": {"text": "constraints", "start": 6, "end": 17}}, {"head": {"text": "algebraic constraints", "start": 69, "end": 90}, "tail": {"text": "constraints", "start": 6, "end": 17}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While conditional independencies are well studied and frequently used in causal induction algorithms , Verma constraints are still poorly understood , and rarely applied .", "output": {"relations": {"used for": [{"head": {"text": "conditional independencies", "start": 6, "end": 32}, "tail": {"text": "causal induction algorithms", "start": 73, "end": 100}}], "compare": [{"head": {"text": "Verma constraints", "start": 103, "end": 120}, "tail": {"text": "conditional independencies", "start": 6, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we examine a special subset of Verma constraints which are easy to understand , easy to identify and easy to apply ; they arise from '' dormant independencies , '' namely , conditional independencies that hold in interventional distributions .", "output": {"relations": {"conjunction": [{"head": {"text": "dormant independencies", "start": 150, "end": 172}, "tail": {"text": "conditional independencies", "start": 187, "end": 213}}], "feature of": [{"head": {"text": "conditional independencies", "start": 187, "end": 213}, "tail": {"text": "interventional distributions", "start": 227, "end": 255}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We give a complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph , such that this independence is identifiable , in other words if it resides in an interventional distribution that can be predicted without resorting to interventions .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 19, "end": 28}, "tail": {"text": "dormant independence", "start": 50, "end": 70}}], "feature of": [{"head": {"text": "interventional distribution", "start": 216, "end": 243}, "tail": {"text": "it", "start": 24, "end": 26}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We further show the usefulness of dormant independencies in model testing and induction by giving an algorithm that uses constraints entailed by dormant independencies to prune extraneous edges from a given causal graph .", "output": {"relations": {"used for": [{"head": {"text": "dormant independencies", "start": 34, "end": 56}, "tail": {"text": "model testing", "start": 60, "end": 73}}, {"head": {"text": "dormant independencies", "start": 34, "end": 56}, "tail": {"text": "induction", "start": 78, "end": 87}}, {"head": {"text": "algorithm", "start": 101, "end": 110}, "tail": {"text": "extraneous edges", "start": 177, "end": 193}}, {"head": {"text": "constraints", "start": 121, "end": 132}, "tail": {"text": "algorithm", "start": 101, "end": 110}}], "conjunction": [{"head": {"text": "model testing", "start": 60, "end": 73}, "tail": {"text": "induction", "start": 78, "end": 87}}], "part of": [{"head": {"text": "extraneous edges", "start": 177, "end": 193}, "tail": {"text": "causal graph", "start": 207, "end": 219}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With the recent popularity of animated GIFs on social media , there is need for ways to index them with rich meta-data .", "output": {"relations": {"feature of": [{"head": {"text": "social media", "start": 47, "end": 59}, "tail": {"text": "animated GIFs", "start": 30, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To advance research on animated GIF understanding , we collected a new dataset , Tumblr GIF -LRB- TGIF -RRB- , with 100K animated GIFs from Tumblr and 120K natural language descriptions obtained via crowdsourcing .", "output": {"relations": {"used for": [{"head": {"text": "dataset", "start": 71, "end": 78}, "tail": {"text": "animated GIF understanding", "start": 23, "end": 49}}, {"head": {"text": "crowdsourcing", "start": 199, "end": 212}, "tail": {"text": "natural language descriptions", "start": 156, "end": 185}}], "conjunction": [{"head": {"text": "natural language descriptions", "start": 156, "end": 185}, "tail": {"text": "animated GIFs", "start": 121, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The motivation for this work is to develop a testbed for image sequence description systems , where the task is to generate natural language descriptions for animated GIFs or video clips .", "output": {"relations": {"used for": [{"head": {"text": "natural language descriptions", "start": 124, "end": 153}, "tail": {"text": "animated GIFs", "start": 158, "end": 171}}, {"head": {"text": "natural language descriptions", "start": 124, "end": 153}, "tail": {"text": "video clips", "start": 175, "end": 186}}], "conjunction": [{"head": {"text": "animated GIFs", "start": 158, "end": 171}, "tail": {"text": "video clips", "start": 175, "end": 186}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To ensure a high quality dataset , we developed a series of novel quality controls to validate free-form text input from crowd-workers .", "output": {"relations": {"used for": [{"head": {"text": "quality controls", "start": 66, "end": 82}, "tail": {"text": "free-form text input", "start": 95, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that there is unambiguous association between visual content and natural language descriptions in our dataset , making it an ideal benchmark for the visual content captioning task .", "output": {"relations": {"conjunction": [{"head": {"text": "visual content", "start": 54, "end": 68}, "tail": {"text": "natural language descriptions", "start": 73, "end": 102}}], "part of": [{"head": {"text": "visual content", "start": 54, "end": 68}, "tail": {"text": "dataset", "start": 110, "end": 117}}, {"head": {"text": "natural language descriptions", "start": 73, "end": 102}, "tail": {"text": "dataset", "start": 110, "end": 117}}], "evaluate for": [{"head": {"text": "it", "start": 127, "end": 129}, "tail": {"text": "visual content captioning task", "start": 157, "end": 187}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We perform extensive statistical analyses to compare our dataset to existing image and video description datasets .", "output": {"relations": {"compare": [{"head": {"text": "dataset", "start": 57, "end": 64}, "tail": {"text": "image and video description datasets", "start": 77, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Next , we provide baseline results on the animated GIF description task , using three representative techniques : nearest neighbor , statistical machine translation , and recurrent neural networks .", "output": {"relations": {"used for": [{"head": {"text": "representative techniques", "start": 86, "end": 111}, "tail": {"text": "animated GIF description task", "start": 42, "end": 71}}], "hyponym of": [{"head": {"text": "nearest neighbor", "start": 114, "end": 130}, "tail": {"text": "representative techniques", "start": 86, "end": 111}}, {"head": {"text": "statistical machine translation", "start": 133, "end": 164}, "tail": {"text": "representative techniques", "start": 86, "end": 111}}, {"head": {"text": "recurrent neural networks", "start": 171, "end": 196}, "tail": {"text": "representative techniques", "start": 86, "end": 111}}], "conjunction": [{"head": {"text": "nearest neighbor", "start": 114, "end": 130}, "tail": {"text": "statistical machine translation", "start": 133, "end": 164}}, {"head": {"text": "statistical machine translation", "start": 133, "end": 164}, "tail": {"text": "recurrent neural networks", "start": 171, "end": 196}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we show that models fine-tuned from our animated GIF description dataset can be helpful for automatic movie description .", "output": {"relations": {"used for": [{"head": {"text": "animated GIF description dataset", "start": 50, "end": 82}, "tail": {"text": "automatic movie description", "start": 102, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Systemic grammar has been used for AI text generation work in the past , but the implementations have tended be ad hoc or inefficient .", "output": {"relations": {"used for": [{"head": {"text": "Systemic grammar", "start": 0, "end": 16}, "tail": {"text": "AI text generation", "start": 35, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an approach to systemic text generation where AI problem solving techniques are applied directly to an unadulterated systemic grammar .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "text generation", "start": 44, "end": 59}}, {"head": {"text": "AI problem solving techniques", "start": 66, "end": 95}, "tail": {"text": "systemic grammar", "start": 137, "end": 153}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This approach is made possible by a special relationship between systemic grammar and problem solving : both are organized primarily as choosing from alternatives .", "output": {"relations": {"conjunction": [{"head": {"text": "systemic grammar", "start": 65, "end": 81}, "tail": {"text": "problem solving", "start": 86, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The result is simple , efficient text generation firmly based in a linguistic theory .", "output": {"relations": {"used for": [{"head": {"text": "linguistic theory", "start": 67, "end": 84}, "tail": {"text": "text generation", "start": 33, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper a novel solution to automatic and unsupervised word sense induction -LRB- WSI -RRB- is introduced .", "output": {"relations": {"used for": [{"head": {"text": "solution", "start": 22, "end": 30}, "tail": {"text": "automatic and unsupervised word sense induction -LRB- WSI -RRB-", "start": 34, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It represents an instantiation of the one sense per collocation observation -LRB- Gale et al. , 1992 -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "one sense per collocation observation", "start": 38, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Like most existing approaches it utilizes clustering of word co-occurrences .", "output": {"relations": {"used for": [{"head": {"text": "clustering of word co-occurrences", "start": 42, "end": 75}, "tail": {"text": "it", "start": 30, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 5, "end": 13}, "tail": {"text": "approaches", "start": 33, "end": 43}}], "used for": [{"head": {"text": "approach", "start": 5, "end": 13}, "tail": {"text": "WSI", "start": 47, "end": 50}}, {"head": {"text": "approaches", "start": 33, "end": 43}, "tail": {"text": "WSI", "start": 47, "end": 50}}, {"head": {"text": "it", "start": 59, "end": 61}, "tail": {"text": "one sense per collocation observation", "start": 89, "end": 126}}, {"head": {"text": "triplets of words", "start": 136, "end": 153}, "tail": {"text": "it", "start": 59, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The combination with a two-step clustering process using sentence co-occurrences as features allows for accurate results .", "output": {"relations": {"used for": [{"head": {"text": "sentence co-occurrences", "start": 57, "end": 80}, "tail": {"text": "two-step clustering process", "start": 23, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Additionally , a novel and likewise automatic and unsupervised evaluation method inspired by Schutze 's -LRB- 1992 -RRB- idea of evaluation of word sense disambiguation algorithms is employed .", "output": {"relations": {"evaluate for": [{"head": {"text": "automatic and unsupervised evaluation method", "start": 36, "end": 80}, "tail": {"text": "word sense disambiguation algorithms", "start": 143, "end": 179}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Offering advantages like reproducability and independency of a given biased gold standard it also enables automatic parameter optimization of the WSI algorithm .", "output": {"relations": {"used for": [{"head": {"text": "automatic parameter optimization", "start": 106, "end": 138}, "tail": {"text": "WSI algorithm", "start": 146, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This abstract describes a natural language system which deals usefully with ungrammatical input and describes some actual and potential applications of it in computer aided second language learning .", "output": {"relations": {"used for": [{"head": {"text": "natural language system", "start": 26, "end": 49}, "tail": {"text": "ungrammatical input", "start": 76, "end": 95}}, {"head": {"text": "it", "start": 72, "end": 74}, "tail": {"text": "computer aided second language learning", "start": 158, "end": 197}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , this is not the only area in which the principles of the system might be used , and the aim in building it was simply to demonstrate the workability of the general mechanism , and provide a framework for assessing developments of it .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 67, "end": 73}, "tail": {"text": "this", "start": 10, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In a motorized vehicle a number of easily measurable signals with frequency components related to the rotational speed of the engine can be found , e.g. , vibrations , electrical system voltage level , and ambient sound .", "output": {"relations": {"used for": [{"head": {"text": "frequency components", "start": 66, "end": 86}, "tail": {"text": "measurable signals", "start": 42, "end": 60}}], "feature of": [{"head": {"text": "frequency components", "start": 66, "end": 86}, "tail": {"text": "rotational speed of the engine", "start": 102, "end": 132}}], "hyponym of": [{"head": {"text": "vibrations", "start": 155, "end": 165}, "tail": {"text": "measurable signals", "start": 42, "end": 60}}, {"head": {"text": "electrical system voltage level", "start": 168, "end": 199}, "tail": {"text": "measurable signals", "start": 42, "end": 60}}, {"head": {"text": "ambient sound", "start": 206, "end": 219}, "tail": {"text": "measurable signals", "start": 42, "end": 60}}], "conjunction": [{"head": {"text": "vibrations", "start": 155, "end": 165}, "tail": {"text": "electrical system voltage level", "start": 168, "end": 199}}, {"head": {"text": "electrical system voltage level", "start": 168, "end": 199}, "tail": {"text": "ambient sound", "start": 206, "end": 219}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These signals could potentially be used to estimate the speed and related states of the vehicle .", "output": {"relations": {"used for": [{"head": {"text": "signals", "start": 6, "end": 13}, "tail": {"text": "speed and related states of the vehicle", "start": 56, "end": 95}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unfortunately , such estimates would typically require the relations -LRB- scale factors -RRB- between the frequency components and the speed for different gears to be known .", "output": {"relations": {"conjunction": [{"head": {"text": "frequency components", "start": 107, "end": 127}, "tail": {"text": "speed", "start": 136, "end": 141}}], "feature of": [{"head": {"text": "speed", "start": 136, "end": 141}, "tail": {"text": "gears", "start": 156, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Consequently , in this article we look at the problem of estimating these gear scale factors from training data consisting only of speed measurements and measurements of the signal in question .", "output": {"relations": {"used for": [{"head": {"text": "training data", "start": 98, "end": 111}, "tail": {"text": "gear scale factors", "start": 74, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The estimation problem is formulated as a maximum likelihood estimation problem and heuristics is used to find initial values for a numerical evaluation of the estimator .", "output": {"relations": {"used for": [{"head": {"text": "maximum likelihood estimation problem", "start": 42, "end": 79}, "tail": {"text": "estimation problem", "start": 4, "end": 22}}, {"head": {"text": "heuristics", "start": 84, "end": 94}, "tail": {"text": "numerical evaluation of the estimator", "start": 132, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , a measurement campaign is conducted and the functionality of the estimation method is verified on real data .", "output": {"relations": {"evaluate for": [{"head": {"text": "real data", "start": 108, "end": 117}, "tail": {"text": "estimation method", "start": 75, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "LPC based speech coders operating at bit rates below 3.0 kbits/sec are usually associated with buzzy or metallic artefacts in the synthetic speech .", "output": {"relations": {"feature of": [{"head": {"text": "bit rates", "start": 37, "end": 46}, "tail": {"text": "LPC based speech coders", "start": 0, "end": 23}}, {"head": {"text": "buzzy or metallic artefacts", "start": 95, "end": 122}, "tail": {"text": "synthetic speech", "start": 130, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper a new LPC vocoder is presented which splits the LPC excitation into two frequency bands using a variable cutoff frequency .", "output": {"relations": {"used for": [{"head": {"text": "variable cutoff frequency", "start": 110, "end": 135}, "tail": {"text": "LPC excitation", "start": 62, "end": 76}}, {"head": {"text": "variable cutoff frequency", "start": 110, "end": 135}, "tail": {"text": "frequency bands", "start": 86, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In doing so the coder 's performance during both mixed voicing speech and speech containing acoustic noise is greatly improved , producing soft natural sounding speech .", "output": {"relations": {"used for": [{"head": {"text": "coder", "start": 16, "end": 21}, "tail": {"text": "soft natural sounding speech", "start": 139, "end": 167}}, {"head": {"text": "mixed voicing speech", "start": 49, "end": 69}, "tail": {"text": "coder", "start": 16, "end": 21}}, {"head": {"text": "speech containing acoustic noise", "start": 74, "end": 106}, "tail": {"text": "coder", "start": 16, "end": 21}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper also describes new parameter determination and quantisation techniques vital to the operation of this coder at such low bit rates .", "output": {"relations": {"conjunction": [{"head": {"text": "parameter determination", "start": 29, "end": 52}, "tail": {"text": "quantisation techniques", "start": 57, "end": 80}}], "used for": [{"head": {"text": "parameter determination", "start": 29, "end": 52}, "tail": {"text": "coder", "start": 112, "end": 117}}, {"head": {"text": "quantisation techniques", "start": 57, "end": 80}, "tail": {"text": "coder", "start": 112, "end": 117}}], "feature of": [{"head": {"text": "low bit rates", "start": 126, "end": 139}, "tail": {"text": "coder", "start": 112, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We consider a problem of blind source separation from a set of instantaneous linear mixtures , where the mixing matrix is unknown .", "output": {"relations": {"used for": [{"head": {"text": "instantaneous linear mixtures", "start": 63, "end": 92}, "tail": {"text": "blind source separation", "start": 25, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It was discovered recently , that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary , dramatically improves the quality of separation .", "output": {"relations": {"used for": [{"head": {"text": "signal dictionary", "start": 120, "end": 137}, "tail": {"text": "sparsity of sources", "start": 49, "end": 68}}], "evaluate for": [{"head": {"text": "quality of separation", "start": 166, "end": 187}, "tail": {"text": "sparsity of sources", "start": 49, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this work we use the property of multi scale transforms , such as wavelet or wavelet packets , to decompose signals into sets of local features with various degrees of sparsity .", "output": {"relations": {"hyponym of": [{"head": {"text": "wavelet or wavelet packets", "start": 69, "end": 95}, "tail": {"text": "multi scale transforms", "start": 36, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The performance of the algorithm is verified on noise-free and noisy data .", "output": {"relations": {"evaluate for": [{"head": {"text": "noise-free and noisy data", "start": 48, "end": 73}, "tail": {"text": "algorithm", "start": 23, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments with simulated signals , musical sounds and images demonstrate significant improvement of separation quality over previously reported results .", "output": {"relations": {"conjunction": [{"head": {"text": "simulated signals", "start": 17, "end": 34}, "tail": {"text": "musical sounds", "start": 37, "end": 51}}, {"head": {"text": "musical sounds", "start": 37, "end": 51}, "tail": {"text": "images", "start": 56, "end": 62}}], "evaluate for": [{"head": {"text": "simulated signals", "start": 17, "end": 34}, "tail": {"text": "separation quality", "start": 102, "end": 120}}, {"head": {"text": "musical sounds", "start": 37, "end": 51}, "tail": {"text": "separation quality", "start": 102, "end": 120}}, {"head": {"text": "images", "start": 56, "end": 62}, "tail": {"text": "separation quality", "start": 102, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we explore multilingual feature-level data sharing via Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features .", "output": {"relations": {"used for": [{"head": {"text": "Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features", "start": 71, "end": 134}, "tail": {"text": "multilingual feature-level data sharing", "start": 27, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Given a set of available source languages , we apply language identification to pick the language most similar to the target language , for more efficient use of multilingual resources .", "output": {"relations": {"used for": [{"head": {"text": "language identification", "start": 53, "end": 76}, "tail": {"text": "multilingual resources", "start": 162, "end": 184}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments with IARPA-Babel languages show that bottleneck features trained on the most similar source language perform better than those trained on all available source languages .", "output": {"relations": {"compare": [{"head": {"text": "those", "start": 137, "end": 142}, "tail": {"text": "bottleneck features", "start": 53, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Further analysis suggests that only data similar to the target language is useful for multilingual training .", "output": {"relations": {"used for": [{"head": {"text": "data", "start": 36, "end": 40}, "tail": {"text": "multilingual training", "start": 86, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This article introduces a bidirectional grammar generation system called feature structure-directed generation , developed for a dialogue translation system .", "output": {"relations": {"used for": [{"head": {"text": "bidirectional grammar generation system", "start": 26, "end": 65}, "tail": {"text": "dialogue translation system", "start": 129, "end": 156}}, {"head": {"text": "feature structure-directed generation", "start": 73, "end": 110}, "tail": {"text": "dialogue translation system", "start": 129, "end": 156}}], "hyponym of": [{"head": {"text": "feature structure-directed generation", "start": 73, "end": 110}, "tail": {"text": "bidirectional grammar generation system", "start": 26, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system utilizes typed feature structures to control the top-down derivation in a declarative way .", "output": {"relations": {"used for": [{"head": {"text": "typed feature structures", "start": 20, "end": 44}, "tail": {"text": "system", "start": 4, "end": 10}}, {"head": {"text": "typed feature structures", "start": 20, "end": 44}, "tail": {"text": "top-down derivation", "start": 60, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This generation system also uses disjunctive feature structures to reduce the number of copies of the derivation tree .", "output": {"relations": {"used for": [{"head": {"text": "disjunctive feature structures", "start": 33, "end": 63}, "tail": {"text": "generation system", "start": 5, "end": 22}}, {"head": {"text": "disjunctive feature structures", "start": 33, "end": 63}, "tail": {"text": "derivation tree", "start": 102, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The grammar for this generator is designed to properly generate the speaker 's intention in a telephone dialogue .", "output": {"relations": {"used for": [{"head": {"text": "grammar", "start": 4, "end": 11}, "tail": {"text": "generator", "start": 21, "end": 30}}, {"head": {"text": "grammar", "start": 4, "end": 11}, "tail": {"text": "speaker 's intention", "start": 68, "end": 88}}], "feature of": [{"head": {"text": "telephone dialogue", "start": 94, "end": 112}, "tail": {"text": "speaker 's intention", "start": 68, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions .", "output": {"relations": {"used for": [{"head": {"text": "Automatic image annotation", "start": 0, "end": 26}, "tail": {"text": "semantic image retrieval", "start": 83, "end": 107}}, {"head": {"text": "text descriptions", "start": 112, "end": 129}, "tail": {"text": "semantic image retrieval", "start": 83, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It concerns a process of automatically labeling the image contents with a pre-defined set of keywords which are exploited to represent the image semantics .", "output": {"relations": {"used for": [{"head": {"text": "keywords", "start": 93, "end": 101}, "tail": {"text": "automatically labeling the image contents", "start": 25, "end": 66}}, {"head": {"text": "keywords", "start": 93, "end": 101}, "tail": {"text": "image semantics", "start": 139, "end": 154}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A Maximum Entropy Model-based approach to the task of automatic image annotation is proposed in this paper .", "output": {"relations": {"used for": [{"head": {"text": "Maximum Entropy Model-based approach", "start": 2, "end": 38}, "tail": {"text": "automatic image annotation", "start": 54, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the phase of training , a basic visual vocabulary consisting of blob-tokens to describe the image content is generated at first ; then the statistical relationship is modeled between the blob-tokens and keywords by a Maximum Entropy Model constructed from the training set of labeled images .", "output": {"relations": {"used for": [{"head": {"text": "visual vocabulary", "start": 35, "end": 52}, "tail": {"text": "image content", "start": 95, "end": 108}}, {"head": {"text": "Maximum Entropy Model", "start": 220, "end": 241}, "tail": {"text": "statistical relationship", "start": 142, "end": 166}}], "part of": [{"head": {"text": "blob-tokens", "start": 67, "end": 78}, "tail": {"text": "visual vocabulary", "start": 35, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the phase of annotation , for an unlabeled image , the most likely associated keywords are predicted in terms of the blob-token set extracted from the given image .", "output": {"relations": {"used for": [{"head": {"text": "blob-token set", "start": 120, "end": 134}, "tail": {"text": "keywords", "start": 81, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We carried out experiments on a medium-sized image collection with about 5000 images from Corel Photo CDs .", "output": {"relations": {"used for": [{"head": {"text": "Corel Photo CDs", "start": 90, "end": 105}, "tail": {"text": "medium-sized image collection", "start": 32, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results demonstrated that the annotation performance of this method outperforms some traditional annotation methods by about 8 % in mean precision , showing a potential of the Maximum Entropy Model in the task of automatic image annotation .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 78, "end": 84}, "tail": {"text": "annotation", "start": 47, "end": 57}}, {"head": {"text": "annotation methods", "start": 114, "end": 132}, "tail": {"text": "annotation", "start": 47, "end": 57}}, {"head": {"text": "Maximum Entropy Model", "start": 193, "end": 214}, "tail": {"text": "automatic image annotation", "start": 230, "end": 256}}], "compare": [{"head": {"text": "method", "start": 78, "end": 84}, "tail": {"text": "annotation methods", "start": 114, "end": 132}}], "evaluate for": [{"head": {"text": "mean precision", "start": 149, "end": 163}, "tail": {"text": "annotation methods", "start": 114, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However most of the works found in the literature have focused on identifying and understanding temporal expressions in newswire texts .", "output": {"relations": {"feature of": [{"head": {"text": "newswire texts", "start": 120, "end": 134}, "tail": {"text": "temporal expressions", "start": 96, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we report our work on anchoring temporal expressions in a novel genre , emails .", "output": {"relations": {"feature of": [{"head": {"text": "emails", "start": 86, "end": 92}, "tail": {"text": "temporal expressions", "start": 46, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The highly under-specified nature of these expressions fits well with our constraint-based representation of time , Time Calculus for Natural Language -LRB- TCNL -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "Time Calculus for Natural Language -LRB- TCNL -RRB-", "start": 116, "end": 167}, "tail": {"text": "constraint-based representation of time", "start": 74, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have developed and evaluated a Temporal Expression Anchoror -LRB- TEA -RRB- , and the result shows that it performs significantly better than the baseline , and compares favorably with some of the closely related work .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 107, "end": 109}, "tail": {"text": "baseline", "start": 149, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We address the problem of populating object category detection datasets with dense , per-object 3D reconstructions , bootstrapped from class labels , ground truth figure-ground segmentations and a small set of keypoint annotations .", "output": {"relations": {"used for": [{"head": {"text": "object category detection datasets", "start": 37, "end": 71}, "tail": {"text": "per-object 3D reconstructions", "start": 85, "end": 114}}, {"head": {"text": "ground truth figure-ground segmentations", "start": 150, "end": 190}, "tail": {"text": "per-object 3D reconstructions", "start": 85, "end": 114}}, {"head": {"text": "keypoint annotations", "start": 210, "end": 230}, "tail": {"text": "per-object 3D reconstructions", "start": 85, "end": 114}}], "conjunction": [{"head": {"text": "ground truth figure-ground segmentations", "start": 150, "end": 190}, "tail": {"text": "keypoint annotations", "start": 210, "end": 230}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion , then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 13, "end": 22}, "tail": {"text": "camera viewpoint", "start": 39, "end": 55}}, {"head": {"text": "algorithm", "start": 13, "end": 22}, "tail": {"text": "object shapes", "start": 110, "end": 123}}, {"head": {"text": "rigid structure-from-motion", "start": 62, "end": 89}, "tail": {"text": "algorithm", "start": 13, "end": 22}}, {"head": {"text": "visual hull proposals", "start": 143, "end": 164}, "tail": {"text": "object shapes", "start": 110, "end": 123}}, {"head": {"text": "loose within-class shape similarity assumptions", "start": 175, "end": 222}, "tail": {"text": "visual hull proposals", "start": 143, "end": 164}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets , PASCAL VOC .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 17, "end": 23}, "tail": {"text": "per-object 3D reconstructions", "start": 54, "end": 83}}, {"head": {"text": "object-category detection datasets", "start": 124, "end": 158}, "tail": {"text": "method", "start": 17, "end": 23}}], "hyponym of": [{"head": {"text": "PASCAL VOC", "start": 161, "end": 171}, "tail": {"text": "object-category detection datasets", "start": 124, "end": 158}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion .", "output": {"relations": {"used for": [{"head": {"text": "Probabilistic models", "start": 0, "end": 20}, "tail": {"text": "modeling and recognition of human motion", "start": 82, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular we focus on methods which represent the human motion model as a triangulated graph .", "output": {"relations": {"used for": [{"head": {"text": "triangulated graph", "start": 78, "end": 96}, "tail": {"text": "human motion model", "start": 54, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance .", "output": {"relations": {"used for": [{"head": {"text": "positions", "start": 49, "end": 58}, "tail": {"text": "models", "start": 28, "end": 34}}, {"head": {"text": "velocities", "start": 63, "end": 73}, "tail": {"text": "models", "start": 28, "end": 34}}], "conjunction": [{"head": {"text": "positions", "start": 49, "end": 58}, "tail": {"text": "velocities", "start": 63, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Moreover , a heuristic approach was commonly used to obtain translation invariance .", "output": {"relations": {"used for": [{"head": {"text": "heuristic approach", "start": 13, "end": 31}, "tail": {"text": "translation invariance", "start": 60, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we suggest an improved approach for learning such models and using them for human motion recognition .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 37, "end": 45}, "tail": {"text": "models", "start": 64, "end": 70}}, {"head": {"text": "them", "start": 81, "end": 85}, "tail": {"text": "human motion recognition", "start": 90, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The suggested approach combines multiple cues , i.e. , positions , velocities and appearance into both the learning and detection phases .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 14, "end": 22}, "tail": {"text": "learning and detection phases", "start": 107, "end": 136}}], "hyponym of": [{"head": {"text": "positions", "start": 55, "end": 64}, "tail": {"text": "cues", "start": 41, "end": 45}}, {"head": {"text": "velocities", "start": 67, "end": 77}, "tail": {"text": "cues", "start": 41, "end": 45}}, {"head": {"text": "appearance", "start": 82, "end": 92}, "tail": {"text": "cues", "start": 41, "end": 45}}], "conjunction": [{"head": {"text": "positions", "start": 55, "end": 64}, "tail": {"text": "velocities", "start": 67, "end": 77}}, {"head": {"text": "velocities", "start": 67, "end": 77}, "tail": {"text": "appearance", "start": 82, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , we introduce global variables in the model , which can represent global properties such as translation , scale or viewpoint .", "output": {"relations": {"used for": [{"head": {"text": "global variables", "start": 27, "end": 43}, "tail": {"text": "model", "start": 51, "end": 56}}, {"head": {"text": "global variables", "start": 27, "end": 43}, "tail": {"text": "global properties", "start": 79, "end": 96}}], "hyponym of": [{"head": {"text": "translation", "start": 105, "end": 116}, "tail": {"text": "global properties", "start": 79, "end": 96}}, {"head": {"text": "scale", "start": 119, "end": 124}, "tail": {"text": "global properties", "start": 79, "end": 96}}, {"head": {"text": "viewpoint", "start": 128, "end": 137}, "tail": {"text": "global properties", "start": 79, "end": 96}}], "conjunction": [{"head": {"text": "translation", "start": 105, "end": 116}, "tail": {"text": "scale", "start": 119, "end": 124}}, {"head": {"text": "scale", "start": 119, "end": 124}, "tail": {"text": "viewpoint", "start": 128, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is learned in an unsupervised manner from un-labelled data .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised manner", "start": 27, "end": 46}, "tail": {"text": "model", "start": 4, "end": 9}}, {"head": {"text": "un-labelled data", "start": 52, "end": 68}, "tail": {"text": "unsupervised manner", "start": 27, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that the suggested hybrid proba-bilistic model -LRB- which combines global variables , like translation , with local variables , like relative positions and appearances of body parts -RRB- , leads to : -LRB- i -RRB- faster convergence of learning phase , -LRB- ii -RRB- robustness to occlusions , and , -LRB- iii -RRB- higher recognition rate .", "output": {"relations": {"used for": [{"head": {"text": "global variables", "start": 76, "end": 92}, "tail": {"text": "hybrid proba-bilistic model", "start": 27, "end": 54}}], "hyponym of": [{"head": {"text": "translation", "start": 100, "end": 111}, "tail": {"text": "global variables", "start": 76, "end": 92}}, {"head": {"text": "relative positions", "start": 142, "end": 160}, "tail": {"text": "local variables", "start": 119, "end": 134}}, {"head": {"text": "appearances of body parts", "start": 165, "end": 190}, "tail": {"text": "local variables", "start": 119, "end": 134}}], "conjunction": [{"head": {"text": "relative positions", "start": 142, "end": 160}, "tail": {"text": "appearances of body parts", "start": 165, "end": 190}}, {"head": {"text": "faster convergence", "start": 224, "end": 242}, "tail": {"text": "robustness", "start": 278, "end": 288}}, {"head": {"text": "robustness", "start": 278, "end": 288}, "tail": {"text": "recognition rate", "start": 334, "end": 350}}], "feature of": [{"head": {"text": "faster convergence", "start": 224, "end": 242}, "tail": {"text": "learning phase", "start": 246, "end": 260}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space .", "output": {"relations": {"conjunction": [{"head": {"text": "Factor analysis", "start": 0, "end": 15}, "tail": {"text": "principal components analysis", "start": 20, "end": 49}}], "used for": [{"head": {"text": "Factor analysis", "start": 0, "end": 15}, "tail": {"text": "linear relationships between observed variables", "start": 71, "end": 118}}, {"head": {"text": "principal components analysis", "start": 20, "end": 49}, "tail": {"text": "linear relationships between observed variables", "start": 71, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a nonlinear generalization of factor analysis , called `` product analy-sis '' , that models the observed variables as a linear combination of products of normally distributed hidden variables .", "output": {"relations": {"used for": [{"head": {"text": "nonlinear generalization of factor analysis", "start": 14, "end": 57}, "tail": {"text": "observed variables", "start": 109, "end": 127}}, {"head": {"text": "linear combination of products of normally distributed hidden variables", "start": 133, "end": 204}, "tail": {"text": "nonlinear generalization of factor analysis", "start": 14, "end": 57}}], "hyponym of": [{"head": {"text": "`` product analy-sis ''", "start": 67, "end": 90}, "tail": {"text": "nonlinear generalization of factor analysis", "start": 14, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Just as factor analysis can be viewed as unsupervised linear regression on unobserved , normally distributed hidden variables , product analysis can be viewed as unsupervised linear regression on products of unobserved , normally distributed hidden variables .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised linear regression", "start": 41, "end": 71}, "tail": {"text": "factor analysis", "start": 8, "end": 23}}, {"head": {"text": "unsupervised linear regression", "start": 41, "end": 71}, "tail": {"text": "product analysis", "start": 128, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The mapping between the data and the hidden space is nonlinear , so we use an approximate variational technique for inference and learning .", "output": {"relations": {"used for": [{"head": {"text": "approximate variational technique", "start": 78, "end": 111}, "tail": {"text": "inference", "start": 116, "end": 125}}, {"head": {"text": "approximate variational technique", "start": 78, "end": 111}, "tail": {"text": "learning", "start": 130, "end": 138}}], "conjunction": [{"head": {"text": "inference", "start": 116, "end": 125}, "tail": {"text": "learning", "start": 130, "end": 138}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since product analysis is a generalization of factor analysis , product analysis always finds a higher data likelihood than factor analysis .", "output": {"relations": {"hyponym of": [{"head": {"text": "product analysis", "start": 6, "end": 22}, "tail": {"text": "generalization of factor analysis", "start": 28, "end": 61}}], "compare": [{"head": {"text": "product analysis", "start": 6, "end": 22}, "tail": {"text": "factor analysis", "start": 46, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We give results on pattern recognition and illumination-invariant image clustering .", "output": {"relations": {"conjunction": [{"head": {"text": "pattern recognition", "start": 19, "end": 38}, "tail": {"text": "illumination-invariant image clustering", "start": 43, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a domain independent strategy for the multimedia articulation of answers elicited by a natural language interface to database query applications .", "output": {"relations": {"used for": [{"head": {"text": "domain independent strategy", "start": 23, "end": 50}, "tail": {"text": "multimedia articulation of answers", "start": 59, "end": 93}}, {"head": {"text": "multimedia articulation of answers", "start": 59, "end": 93}, "tail": {"text": "natural language interface", "start": 108, "end": 134}}, {"head": {"text": "natural language interface", "start": 108, "end": 134}, "tail": {"text": "database query applications", "start": 138, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Multimedia answers include videodisc images and heuristically-produced complete sentences in text or text-to-speech form .", "output": {"relations": {"part of": [{"head": {"text": "videodisc images", "start": 27, "end": 43}, "tail": {"text": "Multimedia answers", "start": 0, "end": 18}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Deictic reference and feedback about the discourse are enabled .", "output": {"relations": {"conjunction": [{"head": {"text": "Deictic reference", "start": 0, "end": 17}, "tail": {"text": "feedback", "start": 22, "end": 30}}], "feature of": [{"head": {"text": "Deictic reference", "start": 0, "end": 17}, "tail": {"text": "discourse", "start": 41, "end": 50}}, {"head": {"text": "feedback", "start": 22, "end": 30}, "tail": {"text": "discourse", "start": 41, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality .", "output": {"relations": {"used for": [{"head": {"text": "LOGON MT demonstrator", "start": 4, "end": 25}, "tail": {"text": "general-purpose NLP components", "start": 59, "end": 89}}], "part of": [{"head": {"text": "general-purpose NLP components", "start": 59, "end": 89}, "tail": {"text": "machine translation pipeline", "start": 97, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The demonstrator embodies an interesting combination of hand-built , symbolic resources and stochastic processes .", "output": {"relations": {"part of": [{"head": {"text": "hand-built , symbolic resources", "start": 56, "end": 87}, "tail": {"text": "demonstrator", "start": 4, "end": 16}}, {"head": {"text": "stochastic processes", "start": 92, "end": 112}, "tail": {"text": "demonstrator", "start": 4, "end": 16}}], "conjunction": [{"head": {"text": "hand-built , symbolic resources", "start": 56, "end": 87}, "tail": {"text": "stochastic processes", "start": 92, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe both the syntax and semantics of a general propositional language of context , and give a Hilbert style proof system for this language .", "output": {"relations": {"conjunction": [{"head": {"text": "syntax", "start": 21, "end": 27}, "tail": {"text": "semantics", "start": 32, "end": 41}}], "feature of": [{"head": {"text": "syntax", "start": 21, "end": 27}, "tail": {"text": "propositional language of context", "start": 55, "end": 88}}, {"head": {"text": "semantics", "start": 32, "end": 41}, "tail": {"text": "propositional language of context", "start": 55, "end": 88}}], "used for": [{"head": {"text": "Hilbert style proof system", "start": 102, "end": 128}, "tail": {"text": "language", "start": 69, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A propositional logic of context extends classical propositional logic in two ways .", "output": {"relations": {"used for": [{"head": {"text": "classical propositional logic", "start": 41, "end": 70}, "tail": {"text": "propositional logic of context", "start": 2, "end": 32}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Image matching is a fundamental problem in Computer Vision .", "output": {"relations": {"hyponym of": [{"head": {"text": "Image matching", "start": 0, "end": 14}, "tail": {"text": "Computer Vision", "start": 43, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In the context of feature-based matching , SIFT and its variants have long excelled in a wide array of applications .", "output": {"relations": {"used for": [{"head": {"text": "SIFT", "start": 43, "end": 47}, "tail": {"text": "feature-based matching", "start": 18, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , for ultra-wide baselines , as in the case of aerial images captured under large camera rotations , the appearance variation goes beyond the reach of SIFT and RANSAC .", "output": {"relations": {"feature of": [{"head": {"text": "large camera rotations", "start": 84, "end": 106}, "tail": {"text": "aerial images", "start": 55, "end": 68}}], "conjunction": [{"head": {"text": "SIFT", "start": 159, "end": 163}, "tail": {"text": "RANSAC", "start": 168, "end": 174}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we propose a data-driven , deep learning-based approach that sidesteps local correspondence by framing the problem as a classification task .", "output": {"relations": {"used for": [{"head": {"text": "classification task", "start": 134, "end": 153}, "tail": {"text": "problem", "start": 121, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We train our models on a dataset of urban aerial imagery consisting of ` same ' and ` different ' pairs , collected for this purpose , and characterize the problem via a human study with annotations from Amazon Mechanical Turk .", "output": {"relations": {"used for": [{"head": {"text": "dataset of urban aerial imagery", "start": 25, "end": 56}, "tail": {"text": "models", "start": 13, "end": 19}}, {"head": {"text": "human study", "start": 170, "end": 181}, "tail": {"text": "problem", "start": 156, "end": 163}}, {"head": {"text": "annotations from Amazon Mechanical Turk", "start": 187, "end": 226}, "tail": {"text": "human study", "start": 170, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy .", "output": {"relations": {"compare": [{"head": {"text": "models", "start": 24, "end": 30}, "tail": {"text": "state-of-the-art", "start": 46, "end": 62}}, {"head": {"text": "models", "start": 24, "end": 30}, "tail": {"text": "human accuracy", "start": 108, "end": 122}}], "evaluate for": [{"head": {"text": "ultra-wide baseline matching", "start": 66, "end": 94}, "tail": {"text": "models", "start": 24, "end": 30}}, {"head": {"text": "ultra-wide baseline matching", "start": 66, "end": 94}, "tail": {"text": "state-of-the-art", "start": 46, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We argue that a more sophisticated and fine-grained annotation in the tree-bank would have very positve effects on stochastic parsers trained on the tree-bank and on grammars induced from the treebank , and it would make the treebank more valuable as a source of data for theoretical linguistic investigations .", "output": {"relations": {"used for": [{"head": {"text": "fine-grained annotation", "start": 39, "end": 62}, "tail": {"text": "stochastic parsers", "start": 115, "end": 133}}, {"head": {"text": "tree-bank", "start": 70, "end": 79}, "tail": {"text": "stochastic parsers", "start": 115, "end": 133}}, {"head": {"text": "treebank", "start": 192, "end": 200}, "tail": {"text": "grammars", "start": 166, "end": 174}}, {"head": {"text": "treebank", "start": 192, "end": 200}, "tail": {"text": "theoretical linguistic investigations", "start": 272, "end": 309}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The information gained from corpus research and the analyses that are proposed are realized in the framework of SILVA , a parsing and extraction tool for German text corpora .", "output": {"relations": {"hyponym of": [{"head": {"text": "SILVA", "start": 112, "end": 117}, "tail": {"text": "parsing and extraction tool", "start": 122, "end": 149}}], "used for": [{"head": {"text": "German text corpora", "start": 154, "end": 173}, "tail": {"text": "SILVA", "start": 112, "end": 117}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While paraphrasing is critical both for interpretation and generation of natural language , current systems use manual or semi-automatic methods to collect paraphrases .", "output": {"relations": {"used for": [{"head": {"text": "paraphrasing", "start": 6, "end": 18}, "tail": {"text": "interpretation and generation of natural language", "start": 40, "end": 89}}, {"head": {"text": "systems", "start": 100, "end": 107}, "tail": {"text": "paraphrases", "start": 156, "end": 167}}, {"head": {"text": "manual or semi-automatic methods", "start": 112, "end": 144}, "tail": {"text": "systems", "start": 100, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised learning algorithm", "start": 14, "end": 45}, "tail": {"text": "identification of paraphrases", "start": 50, "end": 79}}, {"head": {"text": "corpus of multiple English translations", "start": 87, "end": 126}, "tail": {"text": "identification of paraphrases", "start": 50, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "phrasal and single word lexical paraphrases", "start": 20, "end": 63}}, {"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "syntactic paraphrases", "start": 75, "end": 96}}], "conjunction": [{"head": {"text": "phrasal and single word lexical paraphrases", "start": 20, "end": 63}, "tail": {"text": "syntactic paraphrases", "start": 75, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An efficient bit-vector-based CKY-style parser for context-free parsing is presented .", "output": {"relations": {"used for": [{"head": {"text": "bit-vector-based CKY-style parser", "start": 13, "end": 46}, "tail": {"text": "context-free parsing", "start": 51, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The parser computes a compact parse forest representation of the complete set of possible analyses for large treebank grammars and long input sentences .", "output": {"relations": {"used for": [{"head": {"text": "parser", "start": 4, "end": 10}, "tail": {"text": "parse forest representation", "start": 30, "end": 57}}, {"head": {"text": "parse forest representation", "start": 30, "end": 57}, "tail": {"text": "large treebank grammars", "start": 103, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The parser uses bit-vector operations to parallelise the basic parsing operations .", "output": {"relations": {"used for": [{"head": {"text": "bit-vector operations", "start": 16, "end": 37}, "tail": {"text": "parser", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring .", "output": {"relations": {"used for": [{"head": {"text": "partially-blurred-image classification and analysis framework", "start": 29, "end": 90}, "tail": {"text": "automatically detecting images", "start": 95, "end": 125}}], "part of": [{"head": {"text": "blurred regions", "start": 137, "end": 152}, "tail": {"text": "images", "start": 119, "end": 125}}], "conjunction": [{"head": {"text": "blur kernel estimation", "start": 229, "end": 251}, "tail": {"text": "image deblurring", "start": 256, "end": 272}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We develop several blur features modeled by image color , gradient , and spectrum information , and use feature parameter training to robustly classify blurred images .", "output": {"relations": {"used for": [{"head": {"text": "image color", "start": 44, "end": 55}, "tail": {"text": "blur features", "start": 19, "end": 32}}, {"head": {"text": "gradient", "start": 58, "end": 66}, "tail": {"text": "blur features", "start": 19, "end": 32}}, {"head": {"text": "spectrum information", "start": 73, "end": 93}, "tail": {"text": "blur features", "start": 19, "end": 32}}, {"head": {"text": "feature parameter training", "start": 104, "end": 130}, "tail": {"text": "blurred images", "start": 152, "end": 166}}], "conjunction": [{"head": {"text": "image color", "start": 44, "end": 55}, "tail": {"text": "gradient", "start": 58, "end": 66}}, {"head": {"text": "gradient", "start": 58, "end": 66}, "tail": {"text": "spectrum information", "start": 73, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our blur detection is based on image patches , making region-wise training and classification in one image efficient .", "output": {"relations": {"used for": [{"head": {"text": "image patches", "start": 31, "end": 44}, "tail": {"text": "blur detection", "start": 4, "end": 18}}, {"head": {"text": "region-wise training and classification", "start": 54, "end": 93}, "tail": {"text": "blur detection", "start": 4, "end": 18}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments show that our method works satisfactorily on challenging image data , which establishes a technical foundation for solving several computer vision problems , such as motion analysis and image restoration , using the blur information .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 36, "end": 42}, "tail": {"text": "computer vision problems", "start": 153, "end": 177}}, {"head": {"text": "blur information", "start": 238, "end": 254}, "tail": {"text": "method", "start": 36, "end": 42}}], "evaluate for": [{"head": {"text": "image data", "start": 79, "end": 89}, "tail": {"text": "method", "start": 36, "end": 42}}], "hyponym of": [{"head": {"text": "motion analysis", "start": 188, "end": 203}, "tail": {"text": "computer vision problems", "start": 153, "end": 177}}, {"head": {"text": "image restoration", "start": 208, "end": 225}, "tail": {"text": "computer vision problems", "start": 153, "end": 177}}], "conjunction": [{"head": {"text": "motion analysis", "start": 188, "end": 203}, "tail": {"text": "image restoration", "start": 208, "end": 225}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have recently reported on two new word-sense disambiguation systems , one trained on bilingual material -LRB- the Canadian Hansards -RRB- and the other trained on monolingual material -LRB- Roget 's Thesaurus and Grolier 's Encyclopedia -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "one", "start": 73, "end": 76}, "tail": {"text": "word-sense disambiguation systems", "start": 37, "end": 70}}, {"head": {"text": "other", "start": 149, "end": 154}, "tail": {"text": "word-sense disambiguation systems", "start": 37, "end": 70}}, {"head": {"text": "Roget 's Thesaurus", "start": 193, "end": 211}, "tail": {"text": "monolingual material", "start": 166, "end": 186}}, {"head": {"text": "Grolier 's Encyclopedia", "start": 216, "end": 239}, "tail": {"text": "monolingual material", "start": 166, "end": 186}}], "conjunction": [{"head": {"text": "one", "start": 73, "end": 76}, "tail": {"text": "other", "start": 149, "end": 154}}, {"head": {"text": "Roget 's Thesaurus", "start": 193, "end": 211}, "tail": {"text": "Grolier 's Encyclopedia", "start": 216, "end": 239}}], "evaluate for": [{"head": {"text": "bilingual material", "start": 88, "end": 106}, "tail": {"text": "one", "start": 73, "end": 76}}], "used for": [{"head": {"text": "monolingual material", "start": 166, "end": 186}, "tail": {"text": "other", "start": 149, "end": 154}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In addition , it could also be used to help evaluate disambiguation algorithms that did not make use of the discourse constraint .", "output": {"relations": {"evaluate for": [{"head": {"text": "it", "start": 6, "end": 8}, "tail": {"text": "disambiguation algorithms", "start": 53, "end": 78}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We study and compare two novel embedding methods for segmenting feature points of piece-wise planar structures from two -LRB- uncalibrated -RRB- perspective images .", "output": {"relations": {"used for": [{"head": {"text": "embedding methods", "start": 31, "end": 48}, "tail": {"text": "segmenting feature points of piece-wise planar structures", "start": 53, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that a set of different homographies can be embedded in different ways to a higher-dimensional real or complex space , so that each homography corresponds to either a complex bilinear form or a real quadratic form .", "output": {"relations": {"feature of": [{"head": {"text": "higher-dimensional real or complex space", "start": 84, "end": 124}, "tail": {"text": "homographies", "start": 32, "end": 44}}, {"head": {"text": "complex bilinear form", "start": 175, "end": 196}, "tail": {"text": "homography", "start": 140, "end": 150}}, {"head": {"text": "real quadratic form", "start": 202, "end": 221}, "tail": {"text": "homography", "start": 140, "end": 150}}], "conjunction": [{"head": {"text": "complex bilinear form", "start": 175, "end": 196}, "tail": {"text": "real quadratic form", "start": 202, "end": 221}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We give a closed-form segmentation solution for each case by utilizing these properties based on subspace-segmentation methods .", "output": {"relations": {"used for": [{"head": {"text": "subspace-segmentation methods", "start": 97, "end": 126}, "tail": {"text": "closed-form segmentation solution", "start": 10, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These theoretical results show that one can intrinsically segment a piece-wise planar scene from 2-D images without explicitly performing any 3-D reconstruction .", "output": {"relations": {"feature of": [{"head": {"text": "2-D images", "start": 97, "end": 107}, "tail": {"text": "piece-wise planar scene", "start": 68, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Background maintenance is a frequent element of video surveillance systems .", "output": {"relations": {"part of": [{"head": {"text": "Background maintenance", "start": 0, "end": 22}, "tail": {"text": "video surveillance systems", "start": 48, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We develop Wallflower , a three-component system for background maintenance : the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background ; the region-level component fills in homogeneous regions of foreground objects ; and the frame-level component detects sudden , global changes in the image and swaps in better approximations of the background .", "output": {"relations": {"used for": [{"head": {"text": "three-component system", "start": 26, "end": 48}, "tail": {"text": "background maintenance", "start": 53, "end": 75}}, {"head": {"text": "Wiener filtering", "start": 113, "end": 129}, "tail": {"text": "pixel-level component", "start": 82, "end": 103}}, {"head": {"text": "Wiener filtering", "start": 113, "end": 129}, "tail": {"text": "probabilistic predictions of the expected background", "start": 138, "end": 190}}, {"head": {"text": "region-level component", "start": 197, "end": 219}, "tail": {"text": "homogeneous regions of foreground objects", "start": 229, "end": 270}}], "part of": [{"head": {"text": "pixel-level component", "start": 82, "end": 103}, "tail": {"text": "three-component system", "start": 26, "end": 48}}, {"head": {"text": "region-level component", "start": 197, "end": 219}, "tail": {"text": "three-component system", "start": 26, "end": 48}}, {"head": {"text": "frame-level component", "start": 281, "end": 302}, "tail": {"text": "three-component system", "start": 26, "end": 48}}], "conjunction": [{"head": {"text": "pixel-level component", "start": 82, "end": 103}, "tail": {"text": "region-level component", "start": 197, "end": 219}}, {"head": {"text": "region-level component", "start": 197, "end": 219}, "tail": {"text": "frame-level component", "start": 281, "end": 302}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compare our system with 8 other background subtraction algorithms .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 15, "end": 21}, "tail": {"text": "background subtraction algorithms", "start": 35, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur .", "output": {"relations": {"compare": [{"head": {"text": "Wallflower", "start": 0, "end": 10}, "tail": {"text": "algorithms", "start": 43, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we analyze the experimental results and propose normative principles for background maintenance .", "output": {"relations": {"used for": [{"head": {"text": "normative principles", "start": 58, "end": 78}, "tail": {"text": "background maintenance", "start": 83, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Is it possible to use out-of-domain acoustic training data to improve a speech recognizer 's performance on a speciic , independent application ?", "output": {"relations": {"used for": [{"head": {"text": "out-of-domain acoustic training data", "start": 22, "end": 58}, "tail": {"text": "speech recognizer", "start": 72, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our experiments , we use Wallstreet Journal -LRB- WSJ -RRB- data to train a recognizer , which is adapted and evaluated in the Phonebook domain .", "output": {"relations": {"used for": [{"head": {"text": "Wallstreet Journal -LRB- WSJ -RRB- data", "start": 28, "end": 67}, "tail": {"text": "recognizer", "start": 79, "end": 89}}], "evaluate for": [{"head": {"text": "Phonebook domain", "start": 130, "end": 146}, "tail": {"text": "recognizer", "start": 79, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , starting from the WSJ-trained recognizer , how much adaptation data -LRB- taken from the Phonebook training corpus -RRB- is necessary to achieve a reasonable recognition performance in spite of the high degree of mismatch ?", "output": {"relations": {"used for": [{"head": {"text": "WSJ-trained recognizer", "start": 26, "end": 48}, "tail": {"text": "recognition", "start": 166, "end": 177}}, {"head": {"text": "adaptation data", "start": 60, "end": 75}, "tail": {"text": "WSJ-trained recognizer", "start": 26, "end": 48}}], "part of": [{"head": {"text": "adaptation data", "start": 60, "end": 75}, "tail": {"text": "Phonebook training corpus", "start": 97, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Second , is it possible to improve the recognition performance of a Phonebook-trained baseline acoustic model by using additional out-of-domain training data ?", "output": {"relations": {"used for": [{"head": {"text": "Phonebook-trained baseline acoustic model", "start": 68, "end": 109}, "tail": {"text": "recognition", "start": 39, "end": 50}}, {"head": {"text": "out-of-domain training data", "start": 130, "end": 157}, "tail": {"text": "Phonebook-trained baseline acoustic model", "start": 68, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes an approach to full parsing suitable for Information Extraction from texts .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "full parsing", "start": 35, "end": 47}}, {"head": {"text": "full parsing", "start": 35, "end": 47}, "tail": {"text": "Information Extraction", "start": 61, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It was implemented in the IE module of FACILE , a EU project for multilingual text classification and IE .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "IE module", "start": 26, "end": 35}}], "part of": [{"head": {"text": "IE module", "start": 26, "end": 35}, "tail": {"text": "FACILE , a EU project for multilingual text classification and IE", "start": 39, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals , and an evaluation study showing that evidence obtained from shallow processing of the graphic 's caption has a significant impact on the system 's success .", "output": {"relations": {"used for": [{"head": {"text": "communicative signals", "start": 99, "end": 120}, "tail": {"text": "graphic interpretation system", "start": 32, "end": 61}}, {"head": {"text": "shallow processing", "start": 183, "end": 201}, "tail": {"text": "system", "start": 55, "end": 61}}, {"head": {"text": "graphic 's caption", "start": 209, "end": 227}, "tail": {"text": "shallow processing", "start": 183, "end": 201}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Graphical models such as Bayesian Networks -LRB- BNs -RRB- are being increasingly applied to various computer vision problems .", "output": {"relations": {"used for": [{"head": {"text": "Graphical models", "start": 0, "end": 16}, "tail": {"text": "computer vision problems", "start": 101, "end": 125}}], "hyponym of": [{"head": {"text": "Bayesian Networks -LRB- BNs -RRB-", "start": 25, "end": 58}, "tail": {"text": "Graphical models", "start": 0, "end": 16}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data , which proves to be difficult to acquire for many computer vision tasks .", "output": {"relations": {"used for": [{"head": {"text": "representative training data", "start": 114, "end": 142}, "tail": {"text": "BN model parameters", "start": 48, "end": 67}}, {"head": {"text": "representative training data", "start": 114, "end": 142}, "tail": {"text": "computer vision tasks", "start": 194, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "On the other hand , there is often available qualitative prior knowledge about the model .", "output": {"relations": {"feature of": [{"head": {"text": "qualitative prior knowledge", "start": 45, "end": 72}, "tail": {"text": "model", "start": 83, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such knowledge comes either from domain experts based on their experience or from various physical or geometric constraints that govern the objects we try to model .", "output": {"relations": {"used for": [{"head": {"text": "domain experts", "start": 33, "end": 47}, "tail": {"text": "knowledge", "start": 5, "end": 14}}, {"head": {"text": "physical or geometric constraints", "start": 90, "end": 123}, "tail": {"text": "knowledge", "start": 5, "end": 14}}], "conjunction": [{"head": {"text": "domain experts", "start": 33, "end": 47}, "tail": {"text": "physical or geometric constraints", "start": 90, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unlike the quantitative prior , the qualitative prior is often ignored due to the difficulty of incorporating them into the model learning process .", "output": {"relations": {"compare": [{"head": {"text": "quantitative prior", "start": 11, "end": 29}, "tail": {"text": "qualitative prior", "start": 36, "end": 53}}], "part of": [{"head": {"text": "them", "start": 110, "end": 114}, "tail": {"text": "model learning process", "start": 124, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we introduce a closed-form solution to systematically combine the limited training data with some generic qualitative knowledge for BN parameter learning .", "output": {"relations": {"conjunction": [{"head": {"text": "limited training data", "start": 82, "end": 103}, "tail": {"text": "qualitative knowledge", "start": 122, "end": 143}}], "used for": [{"head": {"text": "limited training data", "start": 82, "end": 103}, "tail": {"text": "BN parameter learning", "start": 148, "end": 169}}, {"head": {"text": "qualitative knowledge", "start": 122, "end": 143}, "tail": {"text": "BN parameter learning", "start": 148, "end": 169}}, {"head": {"text": "BN parameter learning", "start": 148, "end": 169}, "tail": {"text": "closed-form solution", "start": 31, "end": 51}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To validate our method , we compare it with the Maximum Likelihood -LRB- ML -RRB- estimation method under sparse data and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 40, "end": 42}, "tail": {"text": "Maximum Likelihood -LRB- ML -RRB- estimation method", "start": 48, "end": 99}}, {"head": {"text": "it", "start": 40, "end": 42}, "tail": {"text": "Expectation Maximization -LRB- EM -RRB- algorithm", "start": 131, "end": 180}}], "used for": [{"head": {"text": "sparse data", "start": 106, "end": 117}, "tail": {"text": "it", "start": 40, "end": 42}}, {"head": {"text": "sparse data", "start": 106, "end": 117}, "tail": {"text": "Maximum Likelihood -LRB- ML -RRB- estimation method", "start": 48, "end": 99}}, {"head": {"text": "incomplete data", "start": 187, "end": 202}, "tail": {"text": "it", "start": 40, "end": 42}}, {"head": {"text": "incomplete data", "start": 187, "end": 202}, "tail": {"text": "Expectation Maximization -LRB- EM -RRB- algorithm", "start": 131, "end": 180}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To further demonstrate its applications for computer vision , we apply it to learn a BN model for facial Action Unit -LRB- AU -RRB- recognition from real image data .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 23, "end": 25}, "tail": {"text": "computer vision", "start": 44, "end": 59}}, {"head": {"text": "it", "start": 23, "end": 25}, "tail": {"text": "BN model", "start": 85, "end": 93}}, {"head": {"text": "BN model", "start": 85, "end": 93}, "tail": {"text": "facial Action Unit -LRB- AU -RRB- recognition", "start": 98, "end": 143}}, {"head": {"text": "real image data", "start": 149, "end": 164}, "tail": {"text": "facial Action Unit -LRB- AU -RRB- recognition", "start": 98, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results show that with simple and generic qualitative constraints and using only a small amount of training data , our method can robustly and accurately estimate the BN model parameters .", "output": {"relations": {"conjunction": [{"head": {"text": "generic qualitative constraints", "start": 51, "end": 82}, "tail": {"text": "training data", "start": 116, "end": 129}}], "used for": [{"head": {"text": "generic qualitative constraints", "start": 51, "end": 82}, "tail": {"text": "method", "start": 136, "end": 142}}, {"head": {"text": "training data", "start": 116, "end": 129}, "tail": {"text": "method", "start": 136, "end": 142}}, {"head": {"text": "method", "start": 136, "end": 142}, "tail": {"text": "BN model parameters", "start": 184, "end": 203}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we introduce a modal language LT for imposing constraints on trees , and an extension LT -LRB- LF -RRB- for imposing constraints on trees decorated with feature structures .", "output": {"relations": {"used for": [{"head": {"text": "modal language LT", "start": 29, "end": 46}, "tail": {"text": "constraints on trees", "start": 60, "end": 80}}, {"head": {"text": "extension LT -LRB- LF -RRB-", "start": 90, "end": 117}, "tail": {"text": "constraints on trees decorated with feature structures", "start": 131, "end": 185}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The motivation for introducing these languages is to provide tools for formalising grammatical frameworks perspicuously , and the paper illustrates this by showing how the leading ideas of GPSG can be captured in LT -LRB- LF -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "languages", "start": 37, "end": 46}, "tail": {"text": "grammatical frameworks", "start": 83, "end": 105}}, {"head": {"text": "GPSG", "start": 189, "end": 193}, "tail": {"text": "LT -LRB- LF -RRB-", "start": 213, "end": 230}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data .", "output": {"relations": {"used for": [{"head": {"text": "clustering", "start": 50, "end": 60}, "tail": {"text": "inducing semantic verb classes", "start": 64, "end": 94}}, {"head": {"text": "undisambiguated corpus data", "start": 100, "end": 127}, "tail": {"text": "clustering", "start": 50, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a new approach which involves clustering subcategorization frame -LRB- SCF -RRB- distributions using the Information Bottleneck and nearest neighbour methods .", "output": {"relations": {"part of": [{"head": {"text": "clustering subcategorization frame -LRB- SCF -RRB- distributions", "start": 42, "end": 106}, "tail": {"text": "approach", "start": 18, "end": 26}}], "used for": [{"head": {"text": "Information Bottleneck and nearest neighbour methods", "start": 117, "end": 169}, "tail": {"text": "clustering subcategorization frame -LRB- SCF -RRB- distributions", "start": 42, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters , offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data .", "output": {"relations": {"used for": [{"head": {"text": "evaluation scheme", "start": 8, "end": 25}, "tail": {"text": "polysemy", "start": 71, "end": 79}}], "evaluate for": [{"head": {"text": "evaluation scheme", "start": 8, "end": 25}, "tail": {"text": "semantically classifying undisambiguated SCF data", "start": 163, "end": 212}}], "feature of": [{"head": {"text": "polysemy", "start": 71, "end": 79}, "tail": {"text": "clusters", "start": 87, "end": 95}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Due to the capacity of pan-tilt-zoom -LRB- PTZ -RRB- cameras to simultaneously cover a panoramic area and maintain high resolution imagery , researches in automated surveillance systems with multiple PTZ cameras have become increasingly important .", "output": {"relations": {"used for": [{"head": {"text": "pan-tilt-zoom -LRB- PTZ -RRB- cameras", "start": 23, "end": 60}, "tail": {"text": "panoramic area", "start": 87, "end": 101}}, {"head": {"text": "pan-tilt-zoom -LRB- PTZ -RRB- cameras", "start": 23, "end": 60}, "tail": {"text": "high resolution imagery", "start": 115, "end": 138}}], "feature of": [{"head": {"text": "PTZ cameras", "start": 200, "end": 211}, "tail": {"text": "automated surveillance systems", "start": 155, "end": 185}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 14, "end": 24}, "tail": {"text": "relative positioning", "start": 108, "end": 128}}, {"head": {"text": "algorithms", "start": 14, "end": 24}, "tail": {"text": "orientation", "start": 133, "end": 144}}, {"head": {"text": "prior knowledge of intrinsic parameters of the PTZ camera", "start": 37, "end": 94}, "tail": {"text": "algorithms", "start": 14, "end": 24}}], "conjunction": [{"head": {"text": "relative positioning", "start": 108, "end": 128}, "tail": {"text": "orientation", "start": 133, "end": 144}}], "feature of": [{"head": {"text": "relative positioning", "start": 108, "end": 128}, "tail": {"text": "PTZ cameras", "start": 160, "end": 171}}, {"head": {"text": "orientation", "start": 133, "end": 144}, "tail": {"text": "PTZ cameras", "start": 160, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To overcome this limitation , we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model .", "output": {"relations": {"used for": [{"head": {"text": "mapping algorithm", "start": 49, "end": 66}, "tail": {"text": "relative positioning", "start": 84, "end": 104}}, {"head": {"text": "mapping algorithm", "start": 49, "end": 66}, "tail": {"text": "orientation", "start": 109, "end": 120}}, {"head": {"text": "unified polynomial model", "start": 156, "end": 180}, "tail": {"text": "mapping algorithm", "start": 49, "end": 66}}], "conjunction": [{"head": {"text": "relative positioning", "start": 84, "end": 104}, "tail": {"text": "orientation", "start": 109, "end": 120}}], "feature of": [{"head": {"text": "relative positioning", "start": 84, "end": 104}, "tail": {"text": "PTZ cameras", "start": 133, "end": 144}}, {"head": {"text": "orientation", "start": 109, "end": 120}, "tail": {"text": "PTZ cameras", "start": 133, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy , as compared with the work of Chen and Wang .", "output": {"relations": {"evaluate for": [{"head": {"text": "computational complexity", "start": 92, "end": 116}, "tail": {"text": "algorithm", "start": 51, "end": 60}}, {"head": {"text": "flexibility", "start": 130, "end": 141}, "tail": {"text": "algorithm", "start": 51, "end": 60}}, {"head": {"text": "pixel accuracy", "start": 176, "end": 190}, "tail": {"text": "algorithm", "start": 51, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This slightly decreased pixel accuracy can be compensated by consistent labeling approaches without added cost for the application of automated surveillance systems along with changing configurations and a larger number of PTZ cameras .", "output": {"relations": {"used for": [{"head": {"text": "consistent labeling approaches", "start": 61, "end": 91}, "tail": {"text": "pixel accuracy", "start": 24, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a new two-pass algorithm for Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition based on the Information Retrieval -LRB- ELVIRCOS -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "two-pass algorithm", "start": 26, "end": 44}, "tail": {"text": "Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition", "start": 49, "end": 132}}, {"head": {"text": "Information Retrieval -LRB- ELVIRCOS -RRB-", "start": 146, "end": 188}, "tail": {"text": "two-pass algorithm", "start": 26, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The principle of this approach is to decompose a recognition process into two passes where the first pass builds the words subset for the second pass recognition by using information retrieval procedure .", "output": {"relations": {"hyponym of": [{"head": {"text": "first pass", "start": 95, "end": 105}, "tail": {"text": "passes", "start": 78, "end": 84}}, {"head": {"text": "second pass recognition", "start": 138, "end": 161}, "tail": {"text": "passes", "start": 78, "end": 84}}], "used for": [{"head": {"text": "information retrieval procedure", "start": 171, "end": 202}, "tail": {"text": "second pass recognition", "start": 138, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Word graph composition for continuous speech is presented .", "output": {"relations": {"used for": [{"head": {"text": "Word graph composition", "start": 0, "end": 22}, "tail": {"text": "continuous speech", "start": 27, "end": 44}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With this approach a high performances for large vocabulary speech recognition can be obtained .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 10, "end": 18}, "tail": {"text": "large vocabulary speech recognition", "start": 43, "end": 78}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties , and clustering of patches aims to detect structures in the remaining regions .", "output": {"relations": {"conjunction": [{"head": {"text": "patch-based clustering algorithms", "start": 79, "end": 112}, "tail": {"text": "one-class classification", "start": 50, "end": 74}}], "used for": [{"head": {"text": "uniform color and texture properties", "start": 175, "end": 211}, "tail": {"text": "one-class classifiers", "start": 119, "end": 140}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Next , the resulting regions are clustered to obtain a codebook of region types , and two models are constructed for scene representation : a '' bag of individual regions '' representation where each region is regarded separately , and a '' bag of region pairs '' representation where regions with particular spatial relationships are considered together .", "output": {"relations": {"used for": [{"head": {"text": "models", "start": 90, "end": 96}, "tail": {"text": "scene representation", "start": 117, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Given these representations , scene classification is done using Bayesian classifiers .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian classifiers", "start": 65, "end": 85}, "tail": {"text": "scene classification", "start": 30, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach .", "output": {"relations": {"evaluate for": [{"head": {"text": "LabelMe data set", "start": 19, "end": 35}, "tail": {"text": "models", "start": 61, "end": 67}}, {"head": {"text": "LabelMe data set", "start": 19, "end": 35}, "tail": {"text": "baseline global feature-based approach", "start": 96, "end": 134}}], "compare": [{"head": {"text": "models", "start": 61, "end": 67}, "tail": {"text": "baseline global feature-based approach", "start": 96, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is designed for use in error correction , with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "error correction", "start": 33, "end": 49}}, {"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "post-processing", "start": 68, "end": 83}}, {"head": {"text": "it", "start": 53, "end": 55}, "tail": {"text": "NLP tasks", "start": 156, "end": 165}}], "part of": [{"head": {"text": "post-processing", "start": 68, "end": 83}, "tail": {"text": "error correction", "start": 33, "end": 49}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an implementation of the model based on finite-state models , demonstrate the model 's ability to significantly reduce character and word error rate , and provide evaluation results involving automatic extraction of translation lexicons from printed text .", "output": {"relations": {"used for": [{"head": {"text": "finite-state models", "start": 51, "end": 70}, "tail": {"text": "model", "start": 36, "end": 41}}, {"head": {"text": "printed text", "start": 253, "end": 265}, "tail": {"text": "automatic extraction of translation lexicons", "start": 203, "end": 247}}], "evaluate for": [{"head": {"text": "character and word error rate", "start": 130, "end": 159}, "tail": {"text": "model", "start": 36, "end": 41}}, {"head": {"text": "automatic extraction of translation lexicons", "start": 203, "end": 247}, "tail": {"text": "model", "start": 36, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a framework for word alignment based on log-linear models .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 13, "end": 22}, "tail": {"text": "word alignment", "start": 27, "end": 41}}, {"head": {"text": "log-linear models", "start": 51, "end": 68}, "tail": {"text": "framework", "start": 13, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "All knowledge sources are treated as feature functions , which depend on the source langauge sentence , the target language sentence and possible additional variables .", "output": {"relations": {"used for": [{"head": {"text": "knowledge sources", "start": 4, "end": 21}, "tail": {"text": "feature functions", "start": 37, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information .", "output": {"relations": {"used for": [{"head": {"text": "Log-linear models", "start": 0, "end": 17}, "tail": {"text": "statistical alignment models", "start": 24, "end": 52}}, {"head": {"text": "syntactic information", "start": 92, "end": 113}, "tail": {"text": "Log-linear models", "start": 0, "end": 17}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we use IBM Model 3 alignment probabilities , POS correspondence , and bilingual dictionary coverage as features .", "output": {"relations": {"conjunction": [{"head": {"text": "IBM Model 3 alignment probabilities", "start": 23, "end": 58}, "tail": {"text": "POS correspondence", "start": 61, "end": 79}}, {"head": {"text": "POS correspondence", "start": 61, "end": 79}, "tail": {"text": "bilingual dictionary coverage", "start": 86, "end": 115}}], "used for": [{"head": {"text": "IBM Model 3 alignment probabilities", "start": 23, "end": 58}, "tail": {"text": "features", "start": 119, "end": 127}}, {"head": {"text": "POS correspondence", "start": 61, "end": 79}, "tail": {"text": "features", "start": 119, "end": 127}}, {"head": {"text": "bilingual dictionary coverage", "start": 86, "end": 115}, "tail": {"text": "features", "start": 119, "end": 127}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our experiments show that log-linear models significantly outperform IBM translation models .", "output": {"relations": {"compare": [{"head": {"text": "log-linear models", "start": 26, "end": 43}, "tail": {"text": "IBM translation models", "start": 69, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Hough voting in a geometric transformation space allows us to realize spatial verification , but remains sensitive to feature detection errors because of the inflexible quan-tization of single feature correspondences .", "output": {"relations": {"used for": [{"head": {"text": "Hough voting", "start": 0, "end": 12}, "tail": {"text": "spatial verification", "start": 70, "end": 90}}], "feature of": [{"head": {"text": "geometric transformation space", "start": 18, "end": 48}, "tail": {"text": "Hough voting", "start": 0, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To handle this problem , we propose a new method , called adaptive dither voting , for robust spatial verification .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 42, "end": 48}, "tail": {"text": "robust spatial verification", "start": 87, "end": 114}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For each correspondence , instead of hard-mapping it to a single transformation , the method augments its description by using multiple dithered transformations that are deterministically generated by the other correspondences .", "output": {"relations": {"used for": [{"head": {"text": "multiple dithered transformations", "start": 127, "end": 160}, "tail": {"text": "method", "start": 86, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also propose exploiting the non-uniformity of a Hough histogram as the spatial similarity to handle multiple matching surfaces .", "output": {"relations": {"feature of": [{"head": {"text": "non-uniformity", "start": 31, "end": 45}, "tail": {"text": "Hough histogram", "start": 51, "end": 66}}], "used for": [{"head": {"text": "non-uniformity", "start": 31, "end": 45}, "tail": {"text": "multiple matching surfaces", "start": 103, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method outperforms its state-of-the-art counterparts in both accuracy and scalability , especially when it comes to the retrieval of small , rotated objects .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "retrieval of small , rotated objects", "start": 124, "end": 160}}], "compare": [{"head": {"text": "counterparts", "start": 44, "end": 56}, "tail": {"text": "method", "start": 4, "end": 10}}], "evaluate for": [{"head": {"text": "accuracy", "start": 65, "end": 73}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "accuracy", "start": 65, "end": 73}, "tail": {"text": "counterparts", "start": 44, "end": 56}}, {"head": {"text": "scalability", "start": 78, "end": 89}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "scalability", "start": 78, "end": 89}, "tail": {"text": "counterparts", "start": 44, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a novel technique called bispectral photo-metric stereo that makes effective use of fluorescence for shape reconstruction .", "output": {"relations": {"used for": [{"head": {"text": "bispectral photo-metric stereo", "start": 36, "end": 66}, "tail": {"text": "shape reconstruction", "start": 112, "end": 132}}, {"head": {"text": "fluorescence", "start": 95, "end": 107}, "tail": {"text": "bispectral photo-metric stereo", "start": 36, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Due to the complexity of its emission process , fluo-rescence tends to be excluded from most algorithms in computer vision and image processing .", "output": {"relations": {"evaluate for": [{"head": {"text": "complexity", "start": 11, "end": 21}, "tail": {"text": "emission process", "start": 29, "end": 45}}], "used for": [{"head": {"text": "algorithms", "start": 93, "end": 103}, "tail": {"text": "computer vision", "start": 107, "end": 122}}, {"head": {"text": "algorithms", "start": 93, "end": 103}, "tail": {"text": "image processing", "start": 127, "end": 143}}], "conjunction": [{"head": {"text": "computer vision", "start": 107, "end": 122}, "tail": {"text": "image processing", "start": 127, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Moreover , fluorescence 's wavelength-shifting property enables us to estimate the shape of an object by applying photomet-ric stereo to emission-only images without suffering from specular reflection .", "output": {"relations": {"used for": [{"head": {"text": "fluorescence 's wavelength-shifting property", "start": 11, "end": 55}, "tail": {"text": "shape", "start": 83, "end": 88}}, {"head": {"text": "photomet-ric stereo", "start": 114, "end": 133}, "tail": {"text": "shape", "start": 83, "end": 88}}, {"head": {"text": "emission-only images", "start": 137, "end": 157}, "tail": {"text": "photomet-ric stereo", "start": 114, "end": 133}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This is the significant advantage of the fluorescence-based method over previous methods based on reflection .", "output": {"relations": {"compare": [{"head": {"text": "methods", "start": 81, "end": 88}, "tail": {"text": "fluorescence-based method", "start": 41, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present an approach for learning a visual representation from the raw spatiotemporal signals in videos .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 30, "end": 38}, "tail": {"text": "visual representation", "start": 54, "end": 75}}, {"head": {"text": "raw spatiotemporal signals in videos", "start": 85, "end": 121}, "tail": {"text": "visual representation", "start": 54, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We formulate our method as an unsupervised sequential verification task , i.e. , we determine whether a sequence of frames from a video is in the correct temporal order .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised sequential verification task", "start": 30, "end": 71}, "tail": {"text": "method", "start": 17, "end": 23}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With this simple task and no semantic labels , we learn a powerful visual representation using a Convolutional Neural Network -LRB- CNN -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "task", "start": 17, "end": 21}, "tail": {"text": "visual representation", "start": 67, "end": 88}}, {"head": {"text": "Convolutional Neural Network -LRB- CNN -RRB-", "start": 97, "end": 141}, "tail": {"text": "visual representation", "start": 67, "end": 88}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The representation contains complementary information to that learned from supervised image datasets like ImageNet .", "output": {"relations": {"part of": [{"head": {"text": "complementary information", "start": 28, "end": 53}, "tail": {"text": "representation", "start": 4, "end": 18}}], "used for": [{"head": {"text": "supervised image datasets", "start": 75, "end": 100}, "tail": {"text": "complementary information", "start": 28, "end": 53}}], "hyponym of": [{"head": {"text": "ImageNet", "start": 106, "end": 114}, "tail": {"text": "supervised image datasets", "start": 75, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Qualitative results show that our method captures information that is temporally varying , such as human pose .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 34, "end": 40}, "tail": {"text": "human pose", "start": 99, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "When used as pre-training for action recognition , our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51 .", "output": {"relations": {"used for": [{"head": {"text": "pre-training", "start": 13, "end": 25}, "tail": {"text": "action recognition", "start": 30, "end": 48}}, {"head": {"text": "our method", "start": 51, "end": 61}, "tail": {"text": "pre-training", "start": 13, "end": 25}}], "compare": [{"head": {"text": "our method", "start": 51, "end": 61}, "tail": {"text": "learning without external data", "start": 91, "end": 121}}], "evaluate for": [{"head": {"text": "benchmark datasets", "start": 125, "end": 143}, "tail": {"text": "our method", "start": 51, "end": 61}}, {"head": {"text": "benchmark datasets", "start": 125, "end": 143}, "tail": {"text": "learning without external data", "start": 91, "end": 121}}], "hyponym of": [{"head": {"text": "UCF101", "start": 149, "end": 155}, "tail": {"text": "benchmark datasets", "start": 125, "end": 143}}, {"head": {"text": "HMDB51", "start": 160, "end": 166}, "tail": {"text": "benchmark datasets", "start": 125, "end": 143}}], "conjunction": [{"head": {"text": "UCF101", "start": 149, "end": 155}, "tail": {"text": "HMDB51", "start": 160, "end": 166}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To demonstrate its sensitivity to human pose , we show results for pose estimation on the FLIC and MPII datasets that are competitive , or better than approaches using significantly more supervision .", "output": {"relations": {"evaluate for": [{"head": {"text": "FLIC and MPII datasets", "start": 90, "end": 112}, "tail": {"text": "pose estimation", "start": 67, "end": 82}}], "used for": [{"head": {"text": "supervision", "start": 187, "end": 198}, "tail": {"text": "approaches", "start": 151, "end": 161}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our method can be combined with supervised representations to provide an additional boost in accuracy .", "output": {"relations": {"conjunction": [{"head": {"text": "supervised representations", "start": 32, "end": 58}, "tail": {"text": "Our method", "start": 0, "end": 10}}], "evaluate for": [{"head": {"text": "accuracy", "start": 93, "end": 101}, "tail": {"text": "Our method", "start": 0, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "`` To explain complex phenomena , an explanation system must be able to select information from a formal representation of domain knowledge , organize the selected information into multisentential discourse plans , and realize the discourse plans in text .", "output": {"relations": {"used for": [{"head": {"text": "explanation system", "start": 37, "end": 55}, "tail": {"text": "discourse plans", "start": 197, "end": 212}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper reports on a seven-year effort to empirically study explanation generation from semantically rich , large-scale knowledge bases .", "output": {"relations": {"used for": [{"head": {"text": "semantically rich , large-scale knowledge bases", "start": 91, "end": 138}, "tail": {"text": "explanation generation", "start": 63, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , it describes a robust explanation system that constructs multisentential and multi-paragraph explanations from the a large-scale knowledge base in the domain of botanical anatomy , physiology , and development .", "output": {"relations": {"used for": [{"head": {"text": "robust explanation system", "start": 31, "end": 56}, "tail": {"text": "multisentential and multi-paragraph explanations", "start": 73, "end": 121}}, {"head": {"text": "large-scale knowledge base", "start": 133, "end": 159}, "tail": {"text": "robust explanation system", "start": 31, "end": 56}}], "feature of": [{"head": {"text": "botanical anatomy", "start": 177, "end": 194}, "tail": {"text": "large-scale knowledge base", "start": 133, "end": 159}}, {"head": {"text": "physiology", "start": 197, "end": 207}, "tail": {"text": "large-scale knowledge base", "start": 133, "end": 159}}, {"head": {"text": "development", "start": 214, "end": 225}, "tail": {"text": "large-scale knowledge base", "start": 133, "end": 159}}], "conjunction": [{"head": {"text": "botanical anatomy", "start": 177, "end": 194}, "tail": {"text": "physiology", "start": 197, "end": 207}}, {"head": {"text": "physiology", "start": 197, "end": 207}, "tail": {"text": "development", "start": 214, "end": 225}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce the evaluation methodology and describe how performance was assessed with this methodology in the most extensive empirical evaluation conducted on an explanation system .", "output": {"relations": {"evaluate for": [{"head": {"text": "methodology", "start": 28, "end": 39}, "tail": {"text": "explanation system", "start": 163, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an operable definition of focus which is argued to be of a cognito-pragmatic nature and explore how it is determined in discourse in a formalized manner .", "output": {"relations": {"feature of": [{"head": {"text": "cognito-pragmatic nature", "start": 70, "end": 94}, "tail": {"text": "operable definition", "start": 14, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For this purpose , a file card model of discourse model and knowledge store is introduced enabling the decomposition and formal representation of its determination process as a programmable algorithm -LRB- FDA -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "FDA", "start": 206, "end": 209}, "tail": {"text": "programmable algorithm", "start": 177, "end": 199}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Interdisciplinary evidence from social and cognitive psychology is cited and the prospect of the integration of focus via FDA as a discourse-level construct into speech synthesis systems , in particular , concept-to-speech systems , is also briefly discussed .", "output": {"relations": {"used for": [{"head": {"text": "FDA", "start": 122, "end": 125}, "tail": {"text": "discourse-level construct", "start": 131, "end": 156}}], "part of": [{"head": {"text": "discourse-level construct", "start": 131, "end": 156}, "tail": {"text": "speech synthesis systems", "start": 162, "end": 186}}], "hyponym of": [{"head": {"text": "concept-to-speech systems", "start": 205, "end": 230}, "tail": {"text": "speech synthesis systems", "start": 162, "end": 186}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Inference in these models involves solving a combinatorial optimization problem , with methods such as graph cuts , belief propagation .", "output": {"relations": {"used for": [{"head": {"text": "Inference", "start": 0, "end": 9}, "tail": {"text": "models", "start": 19, "end": 25}}, {"head": {"text": "methods", "start": 87, "end": 94}, "tail": {"text": "combinatorial optimization problem", "start": 45, "end": 79}}, {"head": {"text": "graph cuts", "start": 103, "end": 113}, "tail": {"text": "methods", "start": 87, "end": 94}}, {"head": {"text": "belief propagation", "start": 116, "end": 134}, "tail": {"text": "methods", "start": 87, "end": 94}}], "part of": [{"head": {"text": "combinatorial optimization problem", "start": 45, "end": 79}, "tail": {"text": "Inference", "start": 0, "end": 9}}], "conjunction": [{"head": {"text": "graph cuts", "start": 103, "end": 113}, "tail": {"text": "belief propagation", "start": 116, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To overcome this , state-of-the-art structured learning methods frame the problem as one of large margin estimation .", "output": {"relations": {"used for": [{"head": {"text": "structured learning methods", "start": 36, "end": 63}, "tail": {"text": "problem", "start": 74, "end": 81}}, {"head": {"text": "large margin estimation", "start": 92, "end": 115}, "tail": {"text": "problem", "start": 74, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Iterative solutions have been proposed to solve the resulting convex optimization problem .", "output": {"relations": {"used for": [{"head": {"text": "Iterative solutions", "start": 0, "end": 19}, "tail": {"text": "convex optimization problem", "start": 62, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how the resulting optimization problem can be reduced to an equivalent convex problem with a small number of constraints , and solve it using an efficient scheme .", "output": {"relations": {"used for": [{"head": {"text": "convex problem", "start": 79, "end": 93}, "tail": {"text": "optimization problem", "start": 26, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Interpreting metaphors is an integral and inescapable process in human understanding of natural language .", "output": {"relations": {"hyponym of": [{"head": {"text": "Interpreting metaphors", "start": 0, "end": 22}, "tail": {"text": "human understanding of natural language", "start": 65, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper discusses a method of analyzing metaphors based on the existence of a small number of generalized metaphor mappings .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 23, "end": 29}, "tail": {"text": "analyzing metaphors", "start": 33, "end": 52}}, {"head": {"text": "generalized metaphor mappings", "start": 97, "end": 126}, "tail": {"text": "analyzing metaphors", "start": 33, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Each generalized metaphor contains a recognition network , a basic mapping , additional transfer mappings , and an implicit intention component .", "output": {"relations": {"part of": [{"head": {"text": "recognition network", "start": 37, "end": 56}, "tail": {"text": "generalized metaphor", "start": 5, "end": 25}}, {"head": {"text": "basic mapping", "start": 61, "end": 74}, "tail": {"text": "generalized metaphor", "start": 5, "end": 25}}, {"head": {"text": "transfer mappings", "start": 88, "end": 105}, "tail": {"text": "generalized metaphor", "start": 5, "end": 25}}, {"head": {"text": "implicit intention component", "start": 115, "end": 143}, "tail": {"text": "generalized metaphor", "start": 5, "end": 25}}], "conjunction": [{"head": {"text": "recognition network", "start": 37, "end": 56}, "tail": {"text": "basic mapping", "start": 61, "end": 74}}, {"head": {"text": "transfer mappings", "start": 88, "end": 105}, "tail": {"text": "basic mapping", "start": 61, "end": 74}}, {"head": {"text": "transfer mappings", "start": 88, "end": 105}, "tail": {"text": "implicit intention component", "start": 115, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is argued that the method reduces metaphor interpretation from a reconstruction to a recognition task .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "metaphor interpretation", "start": 37, "end": 60}}, {"head": {"text": "recognition task", "start": 88, "end": 104}, "tail": {"text": "metaphor interpretation", "start": 37, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This study presents a method to automatically acquire paraphrases using bilingual corpora , which utilizes the bilingual dependency relations obtained by projecting a monolingual dependency parse onto the other language sentence based on statistical alignment techniques .", "output": {"relations": {"used for": [{"head": {"text": "bilingual corpora", "start": 72, "end": 89}, "tail": {"text": "method to automatically acquire paraphrases", "start": 22, "end": 65}}, {"head": {"text": "bilingual dependency relations", "start": 111, "end": 141}, "tail": {"text": "method to automatically acquire paraphrases", "start": 22, "end": 65}}, {"head": {"text": "monolingual dependency parse", "start": 167, "end": 195}, "tail": {"text": "bilingual dependency relations", "start": 111, "end": 141}}, {"head": {"text": "statistical alignment techniques", "start": 238, "end": 270}, "tail": {"text": "bilingual dependency relations", "start": 111, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since the paraphrasing method is capable of clearly disambiguating the sense of an original phrase using the bilingual context of dependency relation , it would be possible to obtain interchangeable paraphrases under a given context .", "output": {"relations": {"used for": [{"head": {"text": "bilingual context of dependency relation", "start": 109, "end": 149}, "tail": {"text": "paraphrasing method", "start": 10, "end": 29}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Also , we provide an advanced method to acquire generalized translation knowledge using the extracted paraphrases .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 30, "end": 36}, "tail": {"text": "generalized translation knowledge", "start": 48, "end": 81}}, {"head": {"text": "paraphrases", "start": 102, "end": 113}, "tail": {"text": "method", "start": 30, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We applied the method to acquire the generalized translation knowledge for Korean-English translation .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 15, "end": 21}, "tail": {"text": "generalized translation knowledge", "start": 37, "end": 70}}, {"head": {"text": "generalized translation knowledge", "start": 37, "end": 70}, "tail": {"text": "Korean-English translation", "start": 75, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Through experiments with parallel corpora of a Korean and English language pairs , we show that our paraphrasing method effectively extracts paraphrases with high precision , 94.3 % and 84.6 % respectively for Korean and English , and the translation knowledge extracted from the bilingual corpora could be generalized successfully using the paraphrases with the 12.5 % compression ratio .", "output": {"relations": {"used for": [{"head": {"text": "paraphrasing method", "start": 100, "end": 119}, "tail": {"text": "paraphrases", "start": 141, "end": 152}}, {"head": {"text": "bilingual corpora", "start": 280, "end": 297}, "tail": {"text": "translation knowledge", "start": 239, "end": 260}}, {"head": {"text": "paraphrases", "start": 141, "end": 152}, "tail": {"text": "translation knowledge", "start": 239, "end": 260}}], "evaluate for": [{"head": {"text": "precision", "start": 163, "end": 172}, "tail": {"text": "paraphrasing method", "start": 100, "end": 119}}, {"head": {"text": "compression ratio", "start": 370, "end": 387}, "tail": {"text": "translation knowledge", "start": 239, "end": 260}}], "conjunction": [{"head": {"text": "Korean", "start": 47, "end": 53}, "tail": {"text": "English", "start": 58, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We provide a logical definition of Minimalist grammars , that are Stabler 's formalization of Chomsky 's minimalist program .", "output": {"relations": {"hyponym of": [{"head": {"text": "Stabler 's formalization of Chomsky 's minimalist program", "start": 66, "end": 123}, "tail": {"text": "logical definition of Minimalist grammars", "start": 13, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our logical definition leads to a neat relation to categorial grammar , -LRB- yielding a treatment of Montague semantics -RRB- , a parsing-as-deduction in a resource sensitive logic , and a learning algorithm from structured data -LRB- based on a typing-algorithm and type-unification -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "logical definition", "start": 4, "end": 22}, "tail": {"text": "Montague semantics", "start": 102, "end": 120}}, {"head": {"text": "parsing-as-deduction", "start": 131, "end": 151}, "tail": {"text": "resource sensitive logic", "start": 157, "end": 181}}, {"head": {"text": "structured data", "start": 214, "end": 229}, "tail": {"text": "learning algorithm", "start": 190, "end": 208}}, {"head": {"text": "typing-algorithm", "start": 247, "end": 263}, "tail": {"text": "learning algorithm", "start": 190, "end": 208}}, {"head": {"text": "type-unification", "start": 268, "end": 284}, "tail": {"text": "learning algorithm", "start": 190, "end": 208}}], "conjunction": [{"head": {"text": "typing-algorithm", "start": 247, "end": 263}, "tail": {"text": "type-unification", "start": 268, "end": 284}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "There are several approaches that model information extraction as a token classification task , using various tagging strategies to combine multiple tokens .", "output": {"relations": {"used for": [{"head": {"text": "approaches", "start": 18, "end": 28}, "tail": {"text": "information extraction", "start": 40, "end": 62}}, {"head": {"text": "tagging strategies", "start": 110, "end": 128}, "tail": {"text": "token classification task", "start": 68, "end": 93}}], "hyponym of": [{"head": {"text": "information extraction", "start": 40, "end": 62}, "tail": {"text": "token classification task", "start": 68, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also introduce a new strategy , called Begin/After tagging or BIA , and show that it is competitive to the best other strategies .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 85, "end": 87}, "tail": {"text": "strategies", "start": 121, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The objective is a generic system of tools , including a core English lexicon , grammar , and concept representations , for building natural language processing -LRB- NLP -RRB- systems for text understanding .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 27, "end": 33}, "tail": {"text": "natural language processing -LRB- NLP -RRB- systems", "start": 133, "end": 184}}, {"head": {"text": "natural language processing -LRB- NLP -RRB- systems", "start": 133, "end": 184}, "tail": {"text": "text understanding", "start": 189, "end": 207}}], "part of": [{"head": {"text": "core English lexicon", "start": 57, "end": 77}, "tail": {"text": "system", "start": 27, "end": 33}}, {"head": {"text": "grammar", "start": 80, "end": 87}, "tail": {"text": "system", "start": 27, "end": 33}}, {"head": {"text": "concept representations", "start": 94, "end": 117}, "tail": {"text": "system", "start": 27, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Systems built with PAKTUS are intended to generate input to knowledge based systems ordata base systems .", "output": {"relations": {"used for": [{"head": {"text": "PAKTUS", "start": 19, "end": 25}, "tail": {"text": "knowledge based systems", "start": 60, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Input to the NLP system is typically derived from an existing electronic message stream , such as a news wire .", "output": {"relations": {"used for": [{"head": {"text": "electronic message stream", "start": 62, "end": 87}, "tail": {"text": "NLP system", "start": 13, "end": 23}}], "hyponym of": [{"head": {"text": "news wire", "start": 100, "end": 109}, "tail": {"text": "electronic message stream", "start": 62, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "PAKTUS supports the adaptation of the generic core to a variety of domains : JINTACCS messages , RAINFORM messages , news reports about a specific type of event , such as financial transfers or terrorist acts , etc. , by acquiring sublanguage and domain-specific grammar , words , conceptual mappings , and discourse patterns .", "output": {"relations": {"used for": [{"head": {"text": "PAKTUS", "start": 0, "end": 6}, "tail": {"text": "JINTACCS messages", "start": 77, "end": 94}}, {"head": {"text": "PAKTUS", "start": 0, "end": 6}, "tail": {"text": "RAINFORM messages", "start": 97, "end": 114}}, {"head": {"text": "PAKTUS", "start": 0, "end": 6}, "tail": {"text": "news reports", "start": 117, "end": 129}}, {"head": {"text": "sublanguage and domain-specific grammar", "start": 231, "end": 270}, "tail": {"text": "PAKTUS", "start": 0, "end": 6}}, {"head": {"text": "words", "start": 273, "end": 278}, "tail": {"text": "PAKTUS", "start": 0, "end": 6}}, {"head": {"text": "conceptual mappings", "start": 281, "end": 300}, "tail": {"text": "PAKTUS", "start": 0, "end": 6}}, {"head": {"text": "discourse patterns", "start": 307, "end": 325}, "tail": {"text": "PAKTUS", "start": 0, "end": 6}}], "conjunction": [{"head": {"text": "JINTACCS messages", "start": 77, "end": 94}, "tail": {"text": "RAINFORM messages", "start": 97, "end": 114}}, {"head": {"text": "RAINFORM messages", "start": 97, "end": 114}, "tail": {"text": "news reports", "start": 117, "end": 129}}, {"head": {"text": "financial transfers", "start": 171, "end": 190}, "tail": {"text": "terrorist acts", "start": 194, "end": 208}}, {"head": {"text": "sublanguage and domain-specific grammar", "start": 231, "end": 270}, "tail": {"text": "words", "start": 273, "end": 278}}, {"head": {"text": "words", "start": 273, "end": 278}, "tail": {"text": "conceptual mappings", "start": 281, "end": 300}}, {"head": {"text": "conceptual mappings", "start": 281, "end": 300}, "tail": {"text": "discourse patterns", "start": 307, "end": 325}}], "feature of": [{"head": {"text": "event", "start": 155, "end": 160}, "tail": {"text": "news reports", "start": 117, "end": 129}}], "hyponym of": [{"head": {"text": "financial transfers", "start": 171, "end": 190}, "tail": {"text": "event", "start": 155, "end": 160}}, {"head": {"text": "terrorist acts", "start": 194, "end": 208}, "tail": {"text": "event", "start": 155, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper the LIMSI recognizer which was evaluated in the ARPA NOV93 CSR test is described , and experimental results on the WSJ and BREF corpora under closely matched conditions are reported .", "output": {"relations": {"evaluate for": [{"head": {"text": "ARPA NOV93 CSR test", "start": 62, "end": 81}, "tail": {"text": "LIMSI recognizer", "start": 18, "end": 34}}, {"head": {"text": "WSJ and BREF corpora", "start": 129, "end": 149}, "tail": {"text": "LIMSI recognizer", "start": 18, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For both corpora word recognition experiments were carried out with vocabularies containing up to 20k words .", "output": {"relations": {"evaluate for": [{"head": {"text": "corpora", "start": 9, "end": 16}, "tail": {"text": "word recognition", "start": 17, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The recognizer makes use of continuous density HMM with Gaussian mixture for acoustic modeling and n-gram statistics estimated on the newspaper texts for language modeling .", "output": {"relations": {"used for": [{"head": {"text": "continuous density HMM", "start": 28, "end": 50}, "tail": {"text": "recognizer", "start": 4, "end": 14}}, {"head": {"text": "continuous density HMM", "start": 28, "end": 50}, "tail": {"text": "acoustic modeling", "start": 77, "end": 94}}, {"head": {"text": "Gaussian mixture", "start": 56, "end": 72}, "tail": {"text": "acoustic modeling", "start": 77, "end": 94}}, {"head": {"text": "n-gram statistics", "start": 99, "end": 116}, "tail": {"text": "recognizer", "start": 4, "end": 14}}, {"head": {"text": "n-gram statistics", "start": 99, "end": 116}, "tail": {"text": "language modeling", "start": 154, "end": 171}}], "conjunction": [{"head": {"text": "continuous density HMM", "start": 28, "end": 50}, "tail": {"text": "Gaussian mixture", "start": 56, "end": 72}}, {"head": {"text": "continuous density HMM", "start": 28, "end": 50}, "tail": {"text": "n-gram statistics", "start": 99, "end": 116}}], "evaluate for": [{"head": {"text": "newspaper texts", "start": 134, "end": 149}, "tail": {"text": "n-gram statistics", "start": 99, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The recognizer uses a time-synchronous graph-search strategy which is shown to still be viable with a 20k-word vocabulary when used with bigram back-off language models .", "output": {"relations": {"used for": [{"head": {"text": "time-synchronous graph-search strategy", "start": 22, "end": 60}, "tail": {"text": "recognizer", "start": 4, "end": 14}}, {"head": {"text": "bigram back-off language models", "start": 137, "end": 168}, "tail": {"text": "recognizer", "start": 4, "end": 14}}], "conjunction": [{"head": {"text": "bigram back-off language models", "start": 137, "end": 168}, "tail": {"text": "time-synchronous graph-search strategy", "start": 22, "end": 60}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A second forward pass , which makes use of a word graph generated with the bigram , incorporates a trigram language model .", "output": {"relations": {"used for": [{"head": {"text": "bigram", "start": 75, "end": 81}, "tail": {"text": "word graph", "start": 45, "end": 55}}], "conjunction": [{"head": {"text": "trigram language model", "start": 99, "end": 121}, "tail": {"text": "word graph", "start": 45, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Acoustic modeling uses cepstrum-based features , context-dependent phone models -LRB- intra and interword -RRB- , phone duration models , and sex-dependent models .", "output": {"relations": {"used for": [{"head": {"text": "cepstrum-based features", "start": 23, "end": 46}, "tail": {"text": "Acoustic modeling", "start": 0, "end": 17}}, {"head": {"text": "context-dependent phone models -LRB- intra and interword -RRB-", "start": 49, "end": 111}, "tail": {"text": "Acoustic modeling", "start": 0, "end": 17}}, {"head": {"text": "phone duration models", "start": 114, "end": 135}, "tail": {"text": "Acoustic modeling", "start": 0, "end": 17}}, {"head": {"text": "sex-dependent models", "start": 142, "end": 162}, "tail": {"text": "Acoustic modeling", "start": 0, "end": 17}}], "conjunction": [{"head": {"text": "cepstrum-based features", "start": 23, "end": 46}, "tail": {"text": "context-dependent phone models -LRB- intra and interword -RRB-", "start": 49, "end": 111}}, {"head": {"text": "context-dependent phone models -LRB- intra and interword -RRB-", "start": 49, "end": 111}, "tail": {"text": "phone duration models", "start": 114, "end": 135}}, {"head": {"text": "phone duration models", "start": 114, "end": 135}, "tail": {"text": "sex-dependent models", "start": 142, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The co-occurrence pattern , a combination of binary or local features , is more discriminative than individual features and has shown its advantages in object , scene , and action recognition .", "output": {"relations": {"used for": [{"head": {"text": "co-occurrence pattern", "start": 4, "end": 25}, "tail": {"text": "object , scene , and action recognition", "start": 152, "end": 191}}], "part of": [{"head": {"text": "binary or local features", "start": 45, "end": 69}, "tail": {"text": "co-occurrence pattern", "start": 4, "end": 25}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Then we propose a novel data mining method to efficiently discover the optimal co-occurrence pattern with minimum empirical error , despite the noisy training dataset .", "output": {"relations": {"used for": [{"head": {"text": "data mining method", "start": 24, "end": 42}, "tail": {"text": "optimal co-occurrence pattern", "start": 71, "end": 100}}, {"head": {"text": "noisy training dataset", "start": 144, "end": 166}, "tail": {"text": "data mining method", "start": 24, "end": 42}}], "feature of": [{"head": {"text": "minimum empirical error", "start": 106, "end": 129}, "tail": {"text": "optimal co-occurrence pattern", "start": 71, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This mining procedure of AND and OR patterns is readily integrated to boosting , which improves the generalization ability over the conventional boosting decision trees and boosting decision stumps .", "output": {"relations": {"used for": [{"head": {"text": "mining procedure", "start": 5, "end": 21}, "tail": {"text": "AND and OR patterns", "start": 25, "end": 44}}], "part of": [{"head": {"text": "AND and OR patterns", "start": 25, "end": 44}, "tail": {"text": "boosting", "start": 70, "end": 78}}], "compare": [{"head": {"text": "boosting", "start": 70, "end": 78}, "tail": {"text": "boosting decision trees", "start": 145, "end": 168}}, {"head": {"text": "boosting", "start": 70, "end": 78}, "tail": {"text": "boosting decision stumps", "start": 173, "end": 197}}], "evaluate for": [{"head": {"text": "generalization ability", "start": 100, "end": 122}, "tail": {"text": "boosting", "start": 70, "end": 78}}, {"head": {"text": "generalization ability", "start": 100, "end": 122}, "tail": {"text": "boosting decision trees", "start": 145, "end": 168}}, {"head": {"text": "generalization ability", "start": 100, "end": 122}, "tail": {"text": "boosting decision stumps", "start": 173, "end": 197}}], "conjunction": [{"head": {"text": "boosting decision trees", "start": 145, "end": 168}, "tail": {"text": "boosting decision stumps", "start": 173, "end": 197}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our versatile experiments on object , scene , and action cat-egorization validate the advantages of the discovered dis-criminative co-occurrence patterns .", "output": {"relations": {"evaluate for": [{"head": {"text": "object , scene , and action cat-egorization", "start": 29, "end": 72}, "tail": {"text": "dis-criminative co-occurrence patterns", "start": 115, "end": 153}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum entropy classifiers , boosting and SVMs are applied to language processing tasks , it is possible to achieve high accuracies , but eventually their performances all tend to plateau out at around the same point .", "output": {"relations": {"used for": [{"head": {"text": "classifiers", "start": 85, "end": 96}, "tail": {"text": "language processing tasks", "start": 168, "end": 193}}], "hyponym of": [{"head": {"text": "maximum entropy classifiers", "start": 105, "end": 132}, "tail": {"text": "classifiers", "start": 85, "end": 96}}, {"head": {"text": "boosting", "start": 135, "end": 143}, "tail": {"text": "classifiers", "start": 85, "end": 96}}, {"head": {"text": "SVMs", "start": 148, "end": 152}, "tail": {"text": "classifiers", "start": 85, "end": 96}}], "conjunction": [{"head": {"text": "maximum entropy classifiers", "start": 105, "end": 132}, "tail": {"text": "boosting", "start": 135, "end": 143}}, {"head": {"text": "boosting", "start": 135, "end": 143}, "tail": {"text": "SVMs", "start": 148, "end": 152}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In recent work , we introduced N-fold Templated Piped Correction , or NTPC -LRB- `` nitpick '' -RRB- , an intriguing error corrector that is designed to work in these extreme operating conditions .", "output": {"relations": {"hyponym of": [{"head": {"text": "N-fold Templated Piped Correction , or NTPC -LRB- `` nitpick '' -RRB-", "start": 31, "end": 100}, "tail": {"text": "error corrector", "start": 117, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Despite its simplicity , it consistently and robustly improves the accuracy of existing highly accurate base models .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 4, "end": 6}, "tail": {"text": "base models", "start": 104, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Focused interaction of this kind is facilitated by a construction-specific approach to flexible parsing , with specialized parsing techniques for each type of construction , and specialized ambiguity representations for each type of ambiguity that a particular construction can give rise to .", "output": {"relations": {"used for": [{"head": {"text": "construction-specific approach", "start": 53, "end": 83}, "tail": {"text": "flexible parsing", "start": 87, "end": 103}}, {"head": {"text": "specialized parsing techniques", "start": 111, "end": 141}, "tail": {"text": "construction", "start": 53, "end": 65}}, {"head": {"text": "ambiguity representations", "start": 190, "end": 215}, "tail": {"text": "ambiguity", "start": 190, "end": 199}}], "conjunction": [{"head": {"text": "construction-specific approach", "start": 53, "end": 83}, "tail": {"text": "specialized parsing techniques", "start": 111, "end": 141}}, {"head": {"text": "specialized parsing techniques", "start": 111, "end": 141}, "tail": {"text": "ambiguity representations", "start": 190, "end": 215}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A construction-specific approach also aids in task-specific language development by allowing a language definition that is natural in terms of the task domain to be interpreted directly without compilation into a uniform grammar formalism , thus greatly speeding the testing of changes to the language definition .", "output": {"relations": {"used for": [{"head": {"text": "construction-specific approach", "start": 2, "end": 32}, "tail": {"text": "task-specific language development", "start": 46, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A proposal to deal with French tenses in the framework of Discourse Representation Theory is presented , as it has been implemented for a fragment at the IMS .", "output": {"relations": {"used for": [{"head": {"text": "Discourse Representation Theory", "start": 58, "end": 89}, "tail": {"text": "French tenses", "start": 24, "end": 37}}, {"head": {"text": "it", "start": 20, "end": 22}, "tail": {"text": "IMS", "start": 154, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is based on the theory of tenses of H. Kamp and Ch .", "output": {"relations": {"used for": [{"head": {"text": "theory of tenses", "start": 19, "end": 35}, "tail": {"text": "It", "start": 0, "end": 2}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Instead of using operators to express the meaning of the tenses the Reichenbachian point of view is adopted and refined such that the impact of the tenses with respect to the meaning of the text is understood as contribution to the integration of the events of a sentence in the event structure of the preceeding text .", "output": {"relations": {"used for": [{"head": {"text": "operators", "start": 17, "end": 26}, "tail": {"text": "meaning of the tenses", "start": 42, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Thereby a system of relevant times provided by the preceeding text and by the temporal adverbials of the sentence being processed is used .", "output": {"relations": {"used for": [{"head": {"text": "preceeding text", "start": 51, "end": 66}, "tail": {"text": "system of relevant times", "start": 10, "end": 34}}, {"head": {"text": "temporal adverbials", "start": 78, "end": 97}, "tail": {"text": "system of relevant times", "start": 10, "end": 34}}], "conjunction": [{"head": {"text": "preceeding text", "start": 51, "end": 66}, "tail": {"text": "temporal adverbials", "start": 78, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This system consists of one or more reference times and temporal perspective times , the speech time and the location time .", "output": {"relations": {"part of": [{"head": {"text": "reference times", "start": 36, "end": 51}, "tail": {"text": "system", "start": 5, "end": 11}}, {"head": {"text": "temporal perspective times", "start": 56, "end": 82}, "tail": {"text": "system", "start": 5, "end": 11}}, {"head": {"text": "speech time", "start": 89, "end": 100}, "tail": {"text": "system", "start": 5, "end": 11}}, {"head": {"text": "location time", "start": 109, "end": 122}, "tail": {"text": "system", "start": 5, "end": 11}}], "conjunction": [{"head": {"text": "reference times", "start": 36, "end": 51}, "tail": {"text": "temporal perspective times", "start": 56, "end": 82}}, {"head": {"text": "temporal perspective times", "start": 56, "end": 82}, "tail": {"text": "speech time", "start": 89, "end": 100}}, {"head": {"text": "speech time", "start": 89, "end": 100}, "tail": {"text": "location time", "start": 109, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In opposition to the approach of Kamp and Rohrer the exact meaning of the tenses is fixed by the resolution component and not in the process of syntactic analysis .", "output": {"relations": {"used for": [{"head": {"text": "resolution component", "start": 97, "end": 117}, "tail": {"text": "meaning of the tenses", "start": 59, "end": 80}}], "compare": [{"head": {"text": "resolution component", "start": 97, "end": 117}, "tail": {"text": "syntactic analysis", "start": 144, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The work presented in this paper is the first step in a project which aims to cluster and summarise electronic discussions in the context of help-desk applications .", "output": {"relations": {"part of": [{"head": {"text": "electronic discussions", "start": 100, "end": 122}, "tail": {"text": "help-desk applications", "start": 141, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we identify features of electronic discussions that influence the clustering process , and offer a filtering mechanism that removes undesirable influences .", "output": {"relations": {"feature of": [{"head": {"text": "features", "start": 28, "end": 36}, "tail": {"text": "electronic discussions", "start": 40, "end": 62}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We tested the clustering and filtering processes on electronic newsgroup discussions , and evaluated their performance by means of two experiments : coarse-level clustering simple information retrieval .", "output": {"relations": {"evaluate for": [{"head": {"text": "electronic newsgroup discussions", "start": 52, "end": 84}, "tail": {"text": "clustering and filtering processes", "start": 14, "end": 48}}, {"head": {"text": "experiments", "start": 135, "end": 146}, "tail": {"text": "clustering and filtering processes", "start": 14, "end": 48}}, {"head": {"text": "experiments", "start": 135, "end": 146}, "tail": {"text": "clustering and filtering processes", "start": 14, "end": 48}}], "hyponym of": [{"head": {"text": "coarse-level clustering", "start": 149, "end": 172}, "tail": {"text": "experiments", "start": 135, "end": 146}}, {"head": {"text": "information retrieval", "start": 180, "end": 201}, "tail": {"text": "experiments", "start": 135, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper presents a method for word sense disambiguation based on parallel corpora .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 21, "end": 27}, "tail": {"text": "word sense disambiguation", "start": 32, "end": 57}}, {"head": {"text": "parallel corpora", "start": 67, "end": 83}, "tail": {"text": "method", "start": 21, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "word alignment", "start": 39, "end": 53}}, {"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "word clustering", "start": 58, "end": 73}}, {"head": {"text": "automatic extraction of translation equivalents", "start": 83, "end": 130}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "aligned wordnets", "start": 164, "end": 180}, "tail": {"text": "method", "start": 4, "end": 10}}], "conjunction": [{"head": {"text": "word alignment", "start": 39, "end": 53}, "tail": {"text": "word clustering", "start": 58, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The same system used in a validation mode , can be used to check and spot alignment errors in multilingually aligned wordnets as BalkaNet and EuroWordNet .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 9, "end": 15}, "tail": {"text": "alignment errors in multilingually aligned wordnets", "start": 74, "end": 125}}], "hyponym of": [{"head": {"text": "BalkaNet", "start": 129, "end": 137}, "tail": {"text": "multilingually aligned wordnets", "start": 94, "end": 125}}, {"head": {"text": "EuroWordNet", "start": 142, "end": 153}, "tail": {"text": "multilingually aligned wordnets", "start": 94, "end": 125}}], "conjunction": [{"head": {"text": "BalkaNet", "start": 129, "end": 137}, "tail": {"text": "EuroWordNet", "start": 142, "end": 153}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper investigates critical configurations for projective reconstruction from multiple images taken by a camera moving in a straight line .", "output": {"relations": {"used for": [{"head": {"text": "images", "start": 92, "end": 98}, "tail": {"text": "projective reconstruction", "start": 52, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Projective reconstruction refers to a determination of the 3D geometrical configuration of a set of 3D points and cameras , given only correspondences between points in the images .", "output": {"relations": {"feature of": [{"head": {"text": "3D geometrical configuration", "start": 59, "end": 87}, "tail": {"text": "3D points and cameras", "start": 100, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Porting a Natural Language Processing -LRB- NLP -RRB- system to a new domain remains one of the bottlenecks in syntactic parsing , because of the amount of effort required to fix gaps in the lexicon , and to attune the existing grammar to the idiosyncracies of the new sublanguage .", "output": {"relations": {"used for": [{"head": {"text": "Natural Language Processing -LRB- NLP -RRB- system", "start": 10, "end": 60}, "tail": {"text": "new domain", "start": 66, "end": 76}}, {"head": {"text": "grammar", "start": 228, "end": 235}, "tail": {"text": "idiosyncracies of the new sublanguage", "start": 243, "end": 280}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper shows how the process of fitting a lexicalized grammar to a domain can be automated to a great extent by using a hybrid system that combines traditional knowledge-based techniques with a corpus-based approach .", "output": {"relations": {"part of": [{"head": {"text": "knowledge-based techniques", "start": 164, "end": 190}, "tail": {"text": "hybrid system", "start": 124, "end": 137}}, {"head": {"text": "corpus-based approach", "start": 198, "end": 219}, "tail": {"text": "hybrid system", "start": 124, "end": 137}}], "conjunction": [{"head": {"text": "knowledge-based techniques", "start": 164, "end": 190}, "tail": {"text": "corpus-based approach", "start": 198, "end": 219}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unification is often the appropriate method for expressing relations between representations in the form of feature structures ; however , there are circumstances in which a different approach is desirable .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 37, "end": 43}, "tail": {"text": "relations between representations", "start": 59, "end": 92}}, {"head": {"text": "feature structures", "start": 108, "end": 126}, "tail": {"text": "relations between representations", "start": 59, "end": 92}}], "compare": [{"head": {"text": "approach", "start": 184, "end": 192}, "tail": {"text": "method", "start": 37, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A declarative formalism is presented which permits direct mappings of one feature structure into another , and illustrative examples are given of its application to areas of current interest .", "output": {"relations": {"feature of": [{"head": {"text": "direct mappings of one feature structure into another", "start": 51, "end": 104}, "tail": {"text": "declarative formalism", "start": 2, "end": 23}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To support engaging human users in robust , mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems , the DARPA Communicator program -LSB- 1 -RSB- is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using .", "output": {"relations": {"used for": [{"head": {"text": "distributed message-passing infrastructure", "start": 229, "end": 271}, "tail": {"text": "dialogue systems", "start": 133, "end": 149}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a novel limited-memory stochastic block BFGS update for incorporating enriched curvature information in stochastic approximation methods .", "output": {"relations": {"used for": [{"head": {"text": "limited-memory stochastic block BFGS update", "start": 19, "end": 62}, "tail": {"text": "incorporating enriched curvature information in stochastic approximation methods", "start": 67, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our method , the estimate of the inverse Hessian matrix that is maintained by it , is updated at each iteration using a sketch of the Hessian , i.e. , a randomly generated compressed form of the Hessian .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 81, "end": 83}, "tail": {"text": "inverse Hessian matrix", "start": 36, "end": 58}}, {"head": {"text": "Hessian", "start": 44, "end": 51}, "tail": {"text": "it", "start": 81, "end": 83}}], "hyponym of": [{"head": {"text": "randomly generated compressed form of the Hessian", "start": 156, "end": 205}, "tail": {"text": "Hessian", "start": 44, "end": 51}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose several sketching strategies , present a new quasi-Newton method that uses stochastic block BFGS updates combined with the variance reduction approach SVRG to compute batch stochastic gradients , and prove linear convergence of the resulting method .", "output": {"relations": {"used for": [{"head": {"text": "quasi-Newton method", "start": 56, "end": 75}, "tail": {"text": "batch stochastic gradients", "start": 178, "end": 204}}, {"head": {"text": "stochastic block BFGS updates", "start": 86, "end": 115}, "tail": {"text": "quasi-Newton method", "start": 56, "end": 75}}, {"head": {"text": "variance reduction approach SVRG", "start": 134, "end": 166}, "tail": {"text": "quasi-Newton method", "start": 56, "end": 75}}], "conjunction": [{"head": {"text": "stochastic block BFGS updates", "start": 86, "end": 115}, "tail": {"text": "variance reduction approach SVRG", "start": 134, "end": 166}}], "feature of": [{"head": {"text": "linear convergence", "start": 217, "end": 235}, "tail": {"text": "method", "start": 69, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Numerical tests on large-scale logistic regression problems reveal that our method is more robust and substantially outperforms current state-of-the-art methods .", "output": {"relations": {"evaluate for": [{"head": {"text": "large-scale logistic regression problems", "start": 19, "end": 59}, "tail": {"text": "method", "start": 76, "end": 82}}, {"head": {"text": "large-scale logistic regression problems", "start": 19, "end": 59}, "tail": {"text": "state-of-the-art methods", "start": 136, "end": 160}}], "compare": [{"head": {"text": "method", "start": 76, "end": 82}, "tail": {"text": "state-of-the-art methods", "start": 136, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The goal of this research is to develop a spoken language system that will demonstrate the usefulness of voice input for interactive problem solving .", "output": {"relations": {"used for": [{"head": {"text": "spoken language system", "start": 42, "end": 64}, "tail": {"text": "interactive problem solving", "start": 121, "end": 148}}, {"head": {"text": "voice input", "start": 105, "end": 116}, "tail": {"text": "interactive problem solving", "start": 121, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Combining speech recognition and natural language processing to achieve speech understanding , the system will be demonstrated in an application domain relevant to the DoD .", "output": {"relations": {"conjunction": [{"head": {"text": "speech recognition", "start": 10, "end": 28}, "tail": {"text": "natural language processing", "start": 33, "end": 60}}], "used for": [{"head": {"text": "speech recognition", "start": 10, "end": 28}, "tail": {"text": "speech understanding", "start": 72, "end": 92}}, {"head": {"text": "natural language processing", "start": 33, "end": 60}, "tail": {"text": "speech understanding", "start": 72, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The objective of this project is to develop a robust and high-performance speech recognition system using a segment-based approach to phonetic recognition .", "output": {"relations": {"used for": [{"head": {"text": "segment-based approach", "start": 108, "end": 130}, "tail": {"text": "robust and high-performance speech recognition system", "start": 46, "end": 99}}, {"head": {"text": "segment-based approach", "start": 108, "end": 130}, "tail": {"text": "phonetic recognition", "start": 134, "end": 154}}, {"head": {"text": "phonetic recognition", "start": 134, "end": 154}, "tail": {"text": "robust and high-performance speech recognition system", "start": 46, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The recognition system will eventually be integrated with natural language processing to achieve spoken language understanding .", "output": {"relations": {"used for": [{"head": {"text": "recognition system", "start": 4, "end": 22}, "tail": {"text": "spoken language understanding", "start": 97, "end": 126}}, {"head": {"text": "natural language processing", "start": 58, "end": 85}, "tail": {"text": "spoken language understanding", "start": 97, "end": 126}}], "conjunction": [{"head": {"text": "natural language processing", "start": 58, "end": 85}, "tail": {"text": "recognition system", "start": 4, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Spelling-checkers have become an integral part of most text processing software .", "output": {"relations": {"part of": [{"head": {"text": "Spelling-checkers", "start": 0, "end": 17}, "tail": {"text": "text processing software", "start": 55, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "From different reasons among which the speed of processing prevails they are usually based on dictionaries of word forms instead of words .", "output": {"relations": {"used for": [{"head": {"text": "dictionaries of word forms", "start": 94, "end": 120}, "tail": {"text": "they", "start": 68, "end": 72}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This approach is sufficient for languages with little inflection such as English , but fails for highly inflective languages such as Czech , Russian , Slovak or other Slavonic languages .", "output": {"relations": {"used for": [{"head": {"text": "languages", "start": 32, "end": 41}, "tail": {"text": "approach", "start": 5, "end": 13}}], "feature of": [{"head": {"text": "inflection", "start": 54, "end": 64}, "tail": {"text": "languages", "start": 32, "end": 41}}], "hyponym of": [{"head": {"text": "English", "start": 73, "end": 80}, "tail": {"text": "languages", "start": 32, "end": 41}}, {"head": {"text": "Czech", "start": 133, "end": 138}, "tail": {"text": "highly inflective languages", "start": 97, "end": 124}}, {"head": {"text": "Russian", "start": 141, "end": 148}, "tail": {"text": "highly inflective languages", "start": 97, "end": 124}}, {"head": {"text": "Slovak", "start": 151, "end": 157}, "tail": {"text": "highly inflective languages", "start": 97, "end": 124}}, {"head": {"text": "Slavonic languages", "start": 167, "end": 185}, "tail": {"text": "highly inflective languages", "start": 97, "end": 124}}], "conjunction": [{"head": {"text": "Czech", "start": 133, "end": 138}, "tail": {"text": "Russian", "start": 141, "end": 148}}, {"head": {"text": "Russian", "start": 141, "end": 148}, "tail": {"text": "Slovak", "start": 151, "end": 157}}, {"head": {"text": "Slovak", "start": 151, "end": 157}, "tail": {"text": "Slavonic languages", "start": 167, "end": 185}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have developed a special method for describing inflection for the purpose of building spelling-checkers for such languages .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 28, "end": 34}, "tail": {"text": "inflection", "start": 50, "end": 60}}, {"head": {"text": "method", "start": 28, "end": 34}, "tail": {"text": "spelling-checkers", "start": 89, "end": 106}}, {"head": {"text": "spelling-checkers", "start": 89, "end": 106}, "tail": {"text": "languages", "start": 116, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The speed of the resulting program lies somewhere in the middle of the scale of existing spelling-checkers for English and the main dictionary fits into the standard 360K floppy , whereas the number of recognized word forms exceeds 6 million -LRB- for Czech -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "English", "start": 111, "end": 118}, "tail": {"text": "spelling-checkers", "start": 89, "end": 106}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Further , a special method has been developed for easy word classification .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 20, "end": 26}, "tail": {"text": "word classification", "start": 55, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a new HMM tagger that exploits context on both sides of a word to be tagged , and evaluate it in both the unsupervised and supervised case .", "output": {"relations": {"evaluate for": [{"head": {"text": "unsupervised and supervised case", "start": 117, "end": 149}, "tail": {"text": "it", "start": 38, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Along the way , we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging , noting that published results to date have not been comparable across corpora or lexicons .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised methods", "start": 65, "end": 85}, "tail": {"text": "part-of-speech tagging", "start": 90, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms , we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 62, "end": 70}, "tail": {"text": "algorithms", "start": 99, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we show how this new tagger achieves state-of-the-art results in a supervised , non-training intensive framework .", "output": {"relations": {"evaluate for": [{"head": {"text": "supervised , non-training intensive framework", "start": 77, "end": 122}, "tail": {"text": "tagger", "start": 31, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient -LRB- SVRG -RRB- and Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "non-uniform sampling strategies", "start": 23, "end": 54}, "tail": {"text": "stochastic optimization algorithms", "start": 87, "end": 121}}], "feature of": [{"head": {"text": "linear convergence", "start": 127, "end": 145}, "tail": {"text": "stochastic optimization algorithms", "start": 87, "end": 121}}], "hyponym of": [{"head": {"text": "Stochastic Variance Reduced Gradient -LRB- SVRG -RRB-", "start": 156, "end": 209}, "tail": {"text": "stochastic optimization algorithms", "start": 87, "end": 121}}, {"head": {"text": "Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-", "start": 214, "end": 264}, "tail": {"text": "stochastic optimization algorithms", "start": 87, "end": 121}}], "conjunction": [{"head": {"text": "Stochastic Variance Reduced Gradient -LRB- SVRG -RRB-", "start": 156, "end": 209}, "tail": {"text": "Stochastic Dual Coordinate Ascent -LRB- SDCA -RRB-", "start": 214, "end": 264}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For a large family of penalized empirical risk minimization problems , our methods exploit data dependent local smoothness of the loss functions near the optimum , while maintaining convergence guarantees .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 75, "end": 82}, "tail": {"text": "penalized empirical risk minimization problems", "start": 22, "end": 68}}, {"head": {"text": "data dependent local smoothness", "start": 91, "end": 122}, "tail": {"text": "methods", "start": 75, "end": 82}}], "feature of": [{"head": {"text": "data dependent local smoothness", "start": 91, "end": 122}, "tail": {"text": "loss functions", "start": 130, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Additionally we present algorithms exploiting local smoothness in more aggressive ways , which perform even better in practice .", "output": {"relations": {"used for": [{"head": {"text": "local smoothness", "start": 46, "end": 62}, "tail": {"text": "algorithms", "start": 24, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Statistical language modeling remains a challenging task , in particular for morphologically rich languages .", "output": {"relations": {"used for": [{"head": {"text": "task", "start": 52, "end": 56}, "tail": {"text": "morphologically rich languages", "start": 77, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recently , new approaches based on factored language models have been developed to address this problem .", "output": {"relations": {"used for": [{"head": {"text": "factored language models", "start": 35, "end": 59}, "tail": {"text": "approaches", "start": 15, "end": 25}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These models provide principled ways of including additional conditioning variables other than the preceding words , such as morphological or syntactic features .", "output": {"relations": {"hyponym of": [{"head": {"text": "morphological or syntactic features", "start": 125, "end": 160}, "tail": {"text": "conditioning variables", "start": 61, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an entirely data-driven model selection procedure based on genetic search , which is shown to outperform both knowledge-based and random selection procedures on two different language modeling tasks -LRB- Arabic and Turkish -RRB- .", "output": {"relations": {"compare": [{"head": {"text": "entirely data-driven model selection procedure", "start": 23, "end": 69}, "tail": {"text": "knowledge-based and random selection procedures", "start": 130, "end": 177}}], "used for": [{"head": {"text": "genetic search", "start": 79, "end": 93}, "tail": {"text": "entirely data-driven model selection procedure", "start": 23, "end": 69}}, {"head": {"text": "knowledge-based and random selection procedures", "start": 130, "end": 177}, "tail": {"text": "language modeling tasks", "start": 195, "end": 218}}], "hyponym of": [{"head": {"text": "Arabic", "start": 225, "end": 231}, "tail": {"text": "language modeling tasks", "start": 195, "end": 218}}, {"head": {"text": "Turkish", "start": 236, "end": 243}, "tail": {"text": "language modeling tasks", "start": 195, "end": 218}}], "conjunction": [{"head": {"text": "Arabic", "start": 225, "end": 231}, "tail": {"text": "Turkish", "start": 236, "end": 243}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems .", "output": {"relations": {"used for": [{"head": {"text": "user modeling", "start": 23, "end": 36}, "tail": {"text": "cooperative responses", "start": 58, "end": 79}}], "part of": [{"head": {"text": "user modeling", "start": 23, "end": 36}, "tail": {"text": "spoken dialogue systems", "start": 96, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unlike previous studies that focus on user 's knowledge or typical kinds of users , the user model we propose is more comprehensive .", "output": {"relations": {"compare": [{"head": {"text": "studies", "start": 16, "end": 23}, "tail": {"text": "user model", "start": 88, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Moreover , the models are automatically derived by decision tree learning using real dialogue data collected by the system .", "output": {"relations": {"used for": [{"head": {"text": "decision tree learning", "start": 51, "end": 73}, "tail": {"text": "models", "start": 15, "end": 21}}, {"head": {"text": "real dialogue data", "start": 80, "end": 98}, "tail": {"text": "decision tree learning", "start": 51, "end": 73}}, {"head": {"text": "system", "start": 116, "end": 122}, "tail": {"text": "real dialogue data", "start": 80, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Dialogue strategies based on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory .", "output": {"relations": {"used for": [{"head": {"text": "Dialogue strategies", "start": 0, "end": 19}, "tail": {"text": "Kyoto city bus information system", "start": 66, "end": 99}}, {"head": {"text": "user modeling", "start": 33, "end": 46}, "tail": {"text": "Dialogue strategies", "start": 0, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a novel method of building polarity-tagged corpus from HTML documents .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 28, "end": 34}, "tail": {"text": "building polarity-tagged corpus", "start": 38, "end": 69}}, {"head": {"text": "HTML documents", "start": 75, "end": 89}, "tail": {"text": "method", "start": 28, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The characteristics of this method is that it is fully automatic and can be applied to arbitrary HTML documents .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 43, "end": 45}, "tail": {"text": "HTML documents", "start": 97, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The idea behind our method is to utilize certain layout structures and linguistic pattern .", "output": {"relations": {"used for": [{"head": {"text": "layout structures", "start": 49, "end": 66}, "tail": {"text": "method", "start": 20, "end": 26}}, {"head": {"text": "linguistic pattern", "start": 71, "end": 89}, "tail": {"text": "method", "start": 20, "end": 26}}], "conjunction": [{"head": {"text": "layout structures", "start": 49, "end": 66}, "tail": {"text": "linguistic pattern", "start": 71, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous work has used monolingual parallel corpora to extract and generate paraphrases .", "output": {"relations": {"used for": [{"head": {"text": "monolingual parallel corpora", "start": 23, "end": 51}, "tail": {"text": "paraphrases", "start": 76, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that this task can be done using bilingual parallel corpora , a much more commonly available resource .", "output": {"relations": {"used for": [{"head": {"text": "bilingual parallel corpora", "start": 41, "end": 67}, "tail": {"text": "task", "start": 18, "end": 22}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using alignment techniques from phrase-based statistical machine translation , we show how paraphrases in one language can be identified using a phrase in another language as a pivot .", "output": {"relations": {"used for": [{"head": {"text": "alignment techniques", "start": 6, "end": 26}, "tail": {"text": "phrase-based statistical machine translation", "start": 32, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities , and show how it can be refined to take contextual information into account .", "output": {"relations": {"part of": [{"head": {"text": "paraphrases", "start": 47, "end": 58}, "tail": {"text": "bilingual parallel corpus", "start": 76, "end": 101}}], "used for": [{"head": {"text": "translation probabilities", "start": 121, "end": 146}, "tail": {"text": "paraphrases", "start": 47, "end": 58}}, {"head": {"text": "contextual information", "start": 188, "end": 210}, "tail": {"text": "it", "start": 31, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate our paraphrase extraction and ranking methods using a set of manual word alignments , and contrast the quality with paraphrases extracted from automatic alignments .", "output": {"relations": {"evaluate for": [{"head": {"text": "manual word alignments", "start": 73, "end": 95}, "tail": {"text": "paraphrase extraction and ranking methods", "start": 16, "end": 57}}, {"head": {"text": "quality", "start": 115, "end": 122}, "tail": {"text": "paraphrases", "start": 128, "end": 139}}], "part of": [{"head": {"text": "paraphrases", "start": 128, "end": 139}, "tail": {"text": "automatic alignments", "start": 155, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes an automatic , essentially domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB- which combines software we have developed for that purpose -LRB- the '' Comparator '' -RRB- and a set of specifications for answer expressions -LRB- the '' Common Answer Specification '' , or CAS -RRB- .", "output": {"relations": {"part of": [{"head": {"text": "software", "start": 141, "end": 149}, "tail": {"text": "domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB-", "start": 47, "end": 125}}, {"head": {"text": "specifications", "start": 231, "end": 245}, "tail": {"text": "domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB-", "start": 47, "end": 125}}], "conjunction": [{"head": {"text": "software", "start": 141, "end": 149}, "tail": {"text": "specifications", "start": 231, "end": 245}}], "used for": [{"head": {"text": "specifications", "start": 231, "end": 245}, "tail": {"text": "answer expressions", "start": 250, "end": 268}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Common Answer Specification determines the syntax of answer expressions , the minimal content that must be included in them , the data to be included in and excluded from test corpora , and the procedures used by the Comparator .", "output": {"relations": {"used for": [{"head": {"text": "Common Answer Specification", "start": 4, "end": 31}, "tail": {"text": "syntax of answer expressions", "start": 47, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes an unsupervised learning method for associative relationships between verb phrases , which is important in developing reliable Q&A systems .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised learning method", "start": 24, "end": 52}, "tail": {"text": "associative relationships between verb phrases", "start": 57, "end": 103}}, {"head": {"text": "associative relationships between verb phrases", "start": 57, "end": 103}, "tail": {"text": "Q&A systems", "start": 148, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our aim is to develop an unsupervised learning method that can obtain such an associative relationship , which we call scenario consistency .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised learning method", "start": 25, "end": 53}, "tail": {"text": "associative relationship", "start": 78, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method we are currently working on uses an expectation-maximization -LRB- EM -RRB- based word-clustering algorithm , and we have evaluated the effectiveness of this method using Japanese verb phrases .", "output": {"relations": {"used for": [{"head": {"text": "expectation-maximization -LRB- EM -RRB- based word-clustering algorithm", "start": 47, "end": 118}, "tail": {"text": "method", "start": 4, "end": 10}}, {"head": {"text": "Japanese verb phrases", "start": 182, "end": 203}, "tail": {"text": "method", "start": 4, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages .", "output": {"relations": {"used for": [{"head": {"text": "text data", "start": 23, "end": 32}, "tail": {"text": "language models", "start": 65, "end": 80}}, {"head": {"text": "language models", "start": 65, "end": 80}, "tail": {"text": "Automatic Speech Recognition", "start": 85, "end": 113}}, {"head": {"text": "language models", "start": 65, "end": 80}, "tail": {"text": "Keyword Search", "start": 118, "end": 132}}, {"head": {"text": "Low Resource Languages", "start": 137, "end": 159}, "tail": {"text": "Automatic Speech Recognition", "start": 85, "end": 113}}, {"head": {"text": "Low Resource Languages", "start": 137, "end": 159}, "tail": {"text": "Keyword Search", "start": 118, "end": 132}}], "feature of": [{"head": {"text": "web", "start": 50, "end": 53}, "tail": {"text": "text data", "start": 23, "end": 32}}], "conjunction": [{"head": {"text": "Automatic Speech Recognition", "start": 85, "end": 113}, "tail": {"text": "Keyword Search", "start": 118, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We scrape text from multiple genres including blogs , online news , translated TED talks , and subtitles .", "output": {"relations": {"hyponym of": [{"head": {"text": "blogs", "start": 46, "end": 51}, "tail": {"text": "genres", "start": 29, "end": 35}}, {"head": {"text": "online news", "start": 54, "end": 65}, "tail": {"text": "genres", "start": 29, "end": 35}}, {"head": {"text": "translated TED talks", "start": 68, "end": 88}, "tail": {"text": "genres", "start": 29, "end": 35}}, {"head": {"text": "subtitles", "start": 95, "end": 104}, "tail": {"text": "genres", "start": 29, "end": 35}}], "conjunction": [{"head": {"text": "blogs", "start": 46, "end": 51}, "tail": {"text": "online news", "start": 54, "end": 65}}, {"head": {"text": "online news", "start": 54, "end": 65}, "tail": {"text": "translated TED talks", "start": 68, "end": 88}}, {"head": {"text": "translated TED talks", "start": 68, "end": 88}, "tail": {"text": "subtitles", "start": 95, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using linearly interpolated language models , we find that blogs and movie subtitles are more relevant for language modeling of conversational telephone speech and obtain large reductions in out-of-vocabulary keywords .", "output": {"relations": {"used for": [{"head": {"text": "linearly interpolated language models", "start": 6, "end": 43}, "tail": {"text": "language modeling of conversational telephone speech", "start": 107, "end": 159}}, {"head": {"text": "blogs", "start": 59, "end": 64}, "tail": {"text": "language modeling of conversational telephone speech", "start": 107, "end": 159}}, {"head": {"text": "movie subtitles", "start": 69, "end": 84}, "tail": {"text": "language modeling of conversational telephone speech", "start": 107, "end": 159}}], "conjunction": [{"head": {"text": "blogs", "start": 59, "end": 64}, "tail": {"text": "movie subtitles", "start": 69, "end": 84}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , we show that the web data can improve Term Error Rate Performance by 3.8 % absolute and Maximum Term-Weighted Value in Keyword Search by 0.0076-0 .1059 absolute points .", "output": {"relations": {"used for": [{"head": {"text": "web data", "start": 31, "end": 39}, "tail": {"text": "Keyword Search", "start": 133, "end": 147}}], "evaluate for": [{"head": {"text": "Term Error Rate Performance", "start": 52, "end": 79}, "tail": {"text": "Keyword Search", "start": 133, "end": 147}}, {"head": {"text": "Maximum Term-Weighted Value", "start": 102, "end": 129}, "tail": {"text": "Keyword Search", "start": 133, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Pipelined Natural Language Generation -LRB- NLG -RRB- systems have grown increasingly complex as architectural modules were added to support language functionalities such as referring expressions , lexical choice , and revision .", "output": {"relations": {"used for": [{"head": {"text": "architectural modules", "start": 97, "end": 118}, "tail": {"text": "language functionalities", "start": 141, "end": 165}}], "hyponym of": [{"head": {"text": "referring expressions", "start": 174, "end": 195}, "tail": {"text": "language functionalities", "start": 141, "end": 165}}, {"head": {"text": "lexical choice", "start": 198, "end": 212}, "tail": {"text": "language functionalities", "start": 141, "end": 165}}, {"head": {"text": "revision", "start": 219, "end": 227}, "tail": {"text": "language functionalities", "start": 141, "end": 165}}], "conjunction": [{"head": {"text": "referring expressions", "start": 174, "end": 195}, "tail": {"text": "lexical choice", "start": 198, "end": 212}}, {"head": {"text": "lexical choice", "start": 198, "end": 212}, "tail": {"text": "revision", "start": 219, "end": 227}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This has given rise to discussions about the relative placement of these new modules in the overall architecture .", "output": {"relations": {"part of": [{"head": {"text": "modules", "start": 77, "end": 84}, "tail": {"text": "overall architecture", "start": 92, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present examples which suggest that in a pipelined NLG architecture , the best approach is to strongly tie it to a revision component .", "output": {"relations": {"conjunction": [{"head": {"text": "it", "start": 62, "end": 64}, "tail": {"text": "revision component", "start": 118, "end": 136}}], "part of": [{"head": {"text": "revision component", "start": 118, "end": 136}, "tail": {"text": "pipelined NLG architecture", "start": 44, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we evaluate the approach in a working multi-page system .", "output": {"relations": {"evaluate for": [{"head": {"text": "multi-page system", "start": 48, "end": 65}, "tail": {"text": "approach", "start": 26, "end": 34}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper a system which understands and conceptualizes scenes descriptions in natural language is presented .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 16, "end": 22}, "tail": {"text": "scenes descriptions in natural language", "start": 60, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , the following components of the system are described : the syntactic analyzer , based on a Procedural Systemic Grammar , the semantic analyzer relying on the Conceptual Dependency Theory , and the dictionary .", "output": {"relations": {"part of": [{"head": {"text": "components", "start": 29, "end": 39}, "tail": {"text": "system", "start": 47, "end": 53}}, {"head": {"text": "syntactic analyzer", "start": 74, "end": 92}, "tail": {"text": "components", "start": 29, "end": 39}}, {"head": {"text": "semantic analyzer", "start": 140, "end": 157}, "tail": {"text": "components", "start": 29, "end": 39}}, {"head": {"text": "dictionary", "start": 212, "end": 222}, "tail": {"text": "components", "start": 29, "end": 39}}], "conjunction": [{"head": {"text": "syntactic analyzer", "start": 74, "end": 92}, "tail": {"text": "semantic analyzer", "start": 140, "end": 157}}, {"head": {"text": "semantic analyzer", "start": 140, "end": 157}, "tail": {"text": "dictionary", "start": 212, "end": 222}}], "used for": [{"head": {"text": "Procedural Systemic Grammar", "start": 106, "end": 133}, "tail": {"text": "syntactic analyzer", "start": 74, "end": 92}}, {"head": {"text": "Conceptual Dependency Theory", "start": 173, "end": 201}, "tail": {"text": "semantic analyzer", "start": 140, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The base parser produces a set of candidate parses for each input sentence , with associated probabilities that define an initial ranking of these parses .", "output": {"relations": {"feature of": [{"head": {"text": "ranking", "start": 130, "end": 137}, "tail": {"text": "parses", "start": 44, "end": 50}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A second model then attempts to improve upon this initial ranking , using additional features of the tree as evidence .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 9, "end": 14}, "tail": {"text": "ranking", "start": 58, "end": 65}}, {"head": {"text": "features", "start": 85, "end": 93}, "tail": {"text": "model", "start": 9, "end": 14}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The strength of our approach is that it allows a tree to be represented as an arbitrary set of features , without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account .", "output": {"relations": {"used for": [{"head": {"text": "features", "start": 95, "end": 103}, "tail": {"text": "generative model", "start": 217, "end": 233}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We introduce a new method for the reranking task , based on the boosting approach to ranking problems described in Freund et al. -LRB- 1998 -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 19, "end": 25}, "tail": {"text": "reranking task", "start": 34, "end": 48}}, {"head": {"text": "boosting approach", "start": 64, "end": 81}, "tail": {"text": "method", "start": 19, "end": 25}}, {"head": {"text": "boosting approach", "start": 64, "end": 81}, "tail": {"text": "ranking problems", "start": 85, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We apply the boosting method to parsing the Wall Street Journal treebank .", "output": {"relations": {"used for": [{"head": {"text": "boosting method", "start": 13, "end": 28}, "tail": {"text": "parsing", "start": 32, "end": 39}}, {"head": {"text": "Wall Street Journal treebank", "start": 44, "end": 72}, "tail": {"text": "boosting method", "start": 13, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method combined the log-likelihood under a baseline model -LRB- that of Collins -LSB- 1999 -RSB- -RRB- with evidence from an additional 500,000 features over parse trees that were not included in the original model .", "output": {"relations": {"part of": [{"head": {"text": "log-likelihood", "start": 24, "end": 38}, "tail": {"text": "method", "start": 4, "end": 10}}], "conjunction": [{"head": {"text": "log-likelihood", "start": 24, "end": 38}, "tail": {"text": "baseline model", "start": 47, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The new model achieved 89.75 % F-measure , a 13 % relative decrease in F-measure error over the baseline model 's score of 88.2 % .", "output": {"relations": {"evaluate for": [{"head": {"text": "F-measure", "start": 31, "end": 40}, "tail": {"text": "model", "start": 8, "end": 13}}, {"head": {"text": "F-measure", "start": 31, "end": 40}, "tail": {"text": "baseline model", "start": 96, "end": 110}}], "compare": [{"head": {"text": "baseline model", "start": 96, "end": 110}, "tail": {"text": "model", "start": 8, "end": 13}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 34, "end": 43}, "tail": {"text": "boosting approach", "start": 52, "end": 69}}, {"head": {"text": "sparsity of the feature space", "start": 99, "end": 128}, "tail": {"text": "algorithm", "start": 34, "end": 43}}], "feature of": [{"head": {"text": "sparsity of the feature space", "start": 99, "end": 128}, "tail": {"text": "parsing data", "start": 136, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach .", "output": {"relations": {"compare": [{"head": {"text": "algorithm", "start": 58, "end": 67}, "tail": {"text": "boosting approach", "start": 107, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We argue that the method is an appealing alternative - in terms of both simplicity and efficiency - to work on feature selection methods within log-linear -LRB- maximum-entropy -RRB- models .", "output": {"relations": {"part of": [{"head": {"text": "feature selection methods", "start": 111, "end": 136}, "tail": {"text": "log-linear -LRB- maximum-entropy -RRB- models", "start": 144, "end": 189}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although the experiments in this article are on natural language parsing -LRB- NLP -RRB- , the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks , for example , speech recognition , machine translation , or natural language generation .", "output": {"relations": {"used for": [{"head": {"text": "ranking tasks", "start": 182, "end": 195}, "tail": {"text": "NLP problems", "start": 139, "end": 151}}], "hyponym of": [{"head": {"text": "speech recognition", "start": 212, "end": 230}, "tail": {"text": "NLP problems", "start": 139, "end": 151}}, {"head": {"text": "speech recognition", "start": 212, "end": 230}, "tail": {"text": "ranking tasks", "start": 182, "end": 195}}, {"head": {"text": "machine translation", "start": 233, "end": 252}, "tail": {"text": "NLP problems", "start": 139, "end": 151}}, {"head": {"text": "machine translation", "start": 233, "end": 252}, "tail": {"text": "ranking tasks", "start": 182, "end": 195}}, {"head": {"text": "natural language generation", "start": 258, "end": 285}, "tail": {"text": "NLP problems", "start": 139, "end": 151}}, {"head": {"text": "natural language generation", "start": 258, "end": 285}, "tail": {"text": "ranking tasks", "start": 182, "end": 195}}], "conjunction": [{"head": {"text": "speech recognition", "start": 212, "end": 230}, "tail": {"text": "machine translation", "start": 233, "end": 252}}, {"head": {"text": "machine translation", "start": 233, "end": 252}, "tail": {"text": "natural language generation", "start": 258, "end": 285}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A model is presented to characterize the class of languages obtained by adding reduplication to context-free languages .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 2, "end": 7}, "tail": {"text": "class of languages", "start": 41, "end": 59}}, {"head": {"text": "reduplication", "start": 79, "end": 92}, "tail": {"text": "context-free languages", "start": 96, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is a pushdown automaton augmented with the ability to check reduplication by using the stack in a new way .", "output": {"relations": {"hyponym of": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "pushdown automaton", "start": 15, "end": 33}}], "used for": [{"head": {"text": "stack", "start": 97, "end": 102}, "tail": {"text": "pushdown automaton", "start": 15, "end": 33}}, {"head": {"text": "stack", "start": 97, "end": 102}, "tail": {"text": "reduplication", "start": 70, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The class of languages generated is shown to lie strictly between the context-free languages and the indexed languages .", "output": {"relations": {"conjunction": [{"head": {"text": "context-free languages", "start": 70, "end": 92}, "tail": {"text": "indexed languages", "start": 101, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model appears capable of accommodating the sort of reduplications that have been observed to occur in natural languages , but it excludes many of the unnatural constructions that other formal models have permitted .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 4, "end": 9}, "tail": {"text": "reduplications", "start": 55, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an image set classification algorithm based on unsupervised clustering of labeled training and unla-beled test data where labels are only used in the stopping criterion .", "output": {"relations": {"used for": [{"head": {"text": "unsupervised clustering", "start": 58, "end": 81}, "tail": {"text": "image set classification algorithm", "start": 14, "end": 48}}, {"head": {"text": "labeled training and unla-beled test data", "start": 85, "end": 126}, "tail": {"text": "unsupervised clustering", "start": 58, "end": 81}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The probability distribution of each class over the set of clusters is used to define a true set based similarity measure .", "output": {"relations": {"used for": [{"head": {"text": "probability distribution", "start": 4, "end": 28}, "tail": {"text": "set based similarity measure", "start": 93, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In each iteration , a proximity matrix is efficiently recomputed to better represent the local subspace structure .", "output": {"relations": {"used for": [{"head": {"text": "proximity matrix", "start": 22, "end": 38}, "tail": {"text": "local subspace structure", "start": 89, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale .", "output": {"relations": {"used for": [{"head": {"text": "Initial clusters", "start": 0, "end": 16}, "tail": {"text": "global data structure", "start": 29, "end": 50}}, {"head": {"text": "finer clusters", "start": 55, "end": 69}, "tail": {"text": "subtle class differences", "start": 102, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Image sets are compactly represented with multiple Grass-mannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm .", "output": {"relations": {"used for": [{"head": {"text": "Grass-mannian manifolds", "start": 51, "end": 74}, "tail": {"text": "Image sets", "start": 0, "end": 10}}, {"head": {"text": "spectral clustering algorithm", "start": 144, "end": 173}, "tail": {"text": "Euclidean space", "start": 110, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results .", "output": {"relations": {"used for": [{"head": {"text": "eigenvector solver", "start": 29, "end": 47}, "tail": {"text": "spectral clustering", "start": 97, "end": 116}}], "evaluate for": [{"head": {"text": "computational cost", "start": 75, "end": 93}, "tail": {"text": "spectral clustering", "start": 97, "end": 116}}, {"head": {"text": "clustering quality", "start": 153, "end": 171}, "tail": {"text": "spectral clustering", "start": 97, "end": 116}}, {"head": {"text": "classification results", "start": 182, "end": 204}, "tail": {"text": "spectral clustering", "start": 97, "end": 116}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation .", "output": {"relations": {"feature of": [{"head": {"text": "computational problems", "start": 29, "end": 51}, "tail": {"text": "probabilistic translation models", "start": 68, "end": 100}}], "used for": [{"head": {"text": "probabilistic translation models", "start": 68, "end": 100}, "tail": {"text": "machine translation", "start": 154, "end": 173}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These models can be viewed as pairs of probabilistic context-free grammars working in a ` synchronous ' way .", "output": {"relations": {"feature of": [{"head": {"text": "probabilistic context-free grammars", "start": 39, "end": 74}, "tail": {"text": "models", "start": 6, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Active shape models are a powerful and widely used tool to interpret complex image data .", "output": {"relations": {"used for": [{"head": {"text": "Active shape models", "start": 0, "end": 19}, "tail": {"text": "complex image data", "start": 69, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By building models of shape variation they enable search algorithms to use a pri-ori knowledge in an efficient and gainful way .", "output": {"relations": {"used for": [{"head": {"text": "search algorithms", "start": 50, "end": 67}, "tail": {"text": "models of shape variation", "start": 12, "end": 37}}, {"head": {"text": "pri-ori knowledge", "start": 77, "end": 94}, "tail": {"text": "search algorithms", "start": 50, "end": 67}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , due to the linearity of PCA , non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably .", "output": {"relations": {"feature of": [{"head": {"text": "linearity", "start": 21, "end": 30}, "tail": {"text": "PCA", "start": 34, "end": 37}}], "hyponym of": [{"head": {"text": "rotations", "start": 61, "end": 70}, "tail": {"text": "non-linearities", "start": 40, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although non-linear extensions of active shape models have been proposed and application specific solutions have been used , they still need a certain amount of user interaction during model building .", "output": {"relations": {"used for": [{"head": {"text": "user interaction", "start": 161, "end": 177}, "tail": {"text": "non-linear extensions of active shape models", "start": 9, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In particular , we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts , each adequate for linear modeling .", "output": {"relations": {"used for": [{"head": {"text": "minimum description length principle", "start": 53, "end": 89}, "tail": {"text": "algorithm", "start": 30, "end": 39}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Which in turn leads to a better model in terms of modes of variations .", "output": {"relations": {"feature of": [{"head": {"text": "modes of variations", "start": 50, "end": 69}, "tail": {"text": "model", "start": 32, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed method is evaluated on synthetic data , medical images and hand contours .", "output": {"relations": {"evaluate for": [{"head": {"text": "synthetic data", "start": 36, "end": 50}, "tail": {"text": "method", "start": 13, "end": 19}}, {"head": {"text": "medical images", "start": 53, "end": 67}, "tail": {"text": "method", "start": 13, "end": 19}}, {"head": {"text": "hand contours", "start": 72, "end": 85}, "tail": {"text": "method", "start": 13, "end": 19}}], "conjunction": [{"head": {"text": "synthetic data", "start": 36, "end": 50}, "tail": {"text": "medical images", "start": 53, "end": 67}}, {"head": {"text": "medical images", "start": 53, "end": 67}, "tail": {"text": "hand contours", "start": 72, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a set of experiments to explore statistical techniques for ranking and selecting the best translations in a graph of translation hypotheses .", "output": {"relations": {"used for": [{"head": {"text": "statistical techniques", "start": 44, "end": 66}, "tail": {"text": "ranking", "start": 71, "end": 78}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In a previous paper -LRB- Carl , 2007 -RRB- we have described how the hypotheses graph is generated through shallow mapping and permutation rules .", "output": {"relations": {"used for": [{"head": {"text": "shallow mapping", "start": 108, "end": 123}, "tail": {"text": "hypotheses graph", "start": 70, "end": 86}}, {"head": {"text": "permutation rules", "start": 128, "end": 145}, "tail": {"text": "hypotheses graph", "start": 70, "end": 86}}], "conjunction": [{"head": {"text": "shallow mapping", "start": 108, "end": 123}, "tail": {"text": "permutation rules", "start": 128, "end": 145}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes a number of methods for elaborating statistical feature functions from some of the vector components .", "output": {"relations": {"used for": [{"head": {"text": "methods", "start": 33, "end": 40}, "tail": {"text": "statistical feature functions", "start": 57, "end": 86}}, {"head": {"text": "vector components", "start": 104, "end": 121}, "tail": {"text": "methods", "start": 33, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The feature functions are trained off-line on different types of text and their log-linear combination is then used to retrieve the best M translation paths in the graph .", "output": {"relations": {"used for": [{"head": {"text": "log-linear combination", "start": 80, "end": 102}, "tail": {"text": "translation paths", "start": 139, "end": 156}}], "part of": [{"head": {"text": "translation paths", "start": 139, "end": 156}, "tail": {"text": "graph", "start": 164, "end": 169}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We compare two language modelling toolkits , the CMU and the SRI toolkit and arrive at three results : 1 -RRB- word-lemma based feature function models produce better results than token-based models , 2 -RRB- adding a PoS-tag feature function to the word-lemma model improves the output and 3 -RRB- weights for lexical translations are suitable if the training material is similar to the texts to be translated .", "output": {"relations": {"hyponym of": [{"head": {"text": "CMU and the SRI toolkit", "start": 49, "end": 72}, "tail": {"text": "language modelling toolkits", "start": 15, "end": 42}}], "compare": [{"head": {"text": "word-lemma based feature function models", "start": 111, "end": 151}, "tail": {"text": "token-based models", "start": 180, "end": 198}}], "part of": [{"head": {"text": "PoS-tag feature function", "start": 218, "end": 242}, "tail": {"text": "word-lemma model", "start": 250, "end": 266}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a specialized editor for a highly structured dictionary .", "output": {"relations": {"used for": [{"head": {"text": "structured dictionary", "start": 54, "end": 75}, "tail": {"text": "editor", "start": 34, "end": 40}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The basic goal in building that editor was to provide an adequate tool to help lexicologists produce a valid and coherent dictionary on the basis of a linguistic theory .", "output": {"relations": {"used for": [{"head": {"text": "editor", "start": 32, "end": 38}, "tail": {"text": "dictionary", "start": 122, "end": 132}}, {"head": {"text": "linguistic theory", "start": 151, "end": 168}, "tail": {"text": "dictionary", "start": 122, "end": 132}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Existing techniques extract term candidates by looking for internal and contextual information associated with domain specific terms .", "output": {"relations": {"feature of": [{"head": {"text": "domain specific terms", "start": 111, "end": 132}, "tail": {"text": "internal and contextual information", "start": 59, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents a novel approach for term extraction based on delimiters which are much more stable and domain independent .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 28, "end": 36}, "tail": {"text": "term extraction", "start": 41, "end": 56}}, {"head": {"text": "delimiters", "start": 66, "end": 76}, "tail": {"text": "approach", "start": 28, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed approach is not as sensitive to term frequency as that of previous works .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 13, "end": 21}, "tail": {"text": "term frequency", "start": 45, "end": 59}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Consequently , the proposed approach can be applied to different domains easily and it is especially useful for resource-limited domains .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 84, "end": 86}, "tail": {"text": "resource-limited domains", "start": 112, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Evaluations conducted on two different domains for Chinese term extraction show significant improvements over existing techniques which verifies its efficiency and domain independent nature .", "output": {"relations": {"evaluate for": [{"head": {"text": "Evaluations", "start": 0, "end": 11}, "tail": {"text": "Chinese term extraction", "start": 51, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments on new term extraction indicate that the proposed approach can also serve as an effective tool for domain lexicon expansion .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 62, "end": 70}, "tail": {"text": "domain lexicon expansion", "start": 111, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a method for identifying systematic patterns in translation data using part-of-speech tag sequences .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 14, "end": 20}, "tail": {"text": "systematic patterns in translation data", "start": 37, "end": 76}}, {"head": {"text": "part-of-speech tag sequences", "start": 83, "end": 111}, "tail": {"text": "method", "start": 14, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems , and demonstrate how our application can be used by developers to explore patterns in machine translation output .", "output": {"relations": {"part of": [{"head": {"text": "analysis", "start": 20, "end": 28}, "tail": {"text": "diagnostic tool", "start": 36, "end": 51}}], "used for": [{"head": {"text": "diagnostic tool", "start": 36, "end": 51}, "tail": {"text": "machine translation systems", "start": 79, "end": 106}}, {"head": {"text": "application", "start": 133, "end": 144}, "tail": {"text": "patterns in machine translation output", "start": 182, "end": 220}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We study the number of hidden layers required by a multilayer neu-ral network with threshold units to compute a function f from n d to -LCB- O , I -RCB- .", "output": {"relations": {"used for": [{"head": {"text": "number of hidden layers", "start": 13, "end": 36}, "tail": {"text": "multilayer neu-ral network", "start": 51, "end": 77}}, {"head": {"text": "threshold units", "start": 83, "end": 98}, "tail": {"text": "number of hidden layers", "start": 13, "end": 36}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that adding these conditions to Gib-son 's assumptions is not sufficient to ensure global computability with one hidden layer , by exhibiting a new non-local configuration , the `` critical cycle '' , which implies that f is not computable with one hidden layer .", "output": {"relations": {"hyponym of": [{"head": {"text": "`` critical cycle ''", "start": 186, "end": 206}, "tail": {"text": "non-local configuration", "start": 156, "end": 179}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an approach to estimate the intrinsic texture properties -LRB- albedo , shading , normal -RRB- of scenes from multiple view acquisition under unknown illumination conditions .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "intrinsic texture properties -LRB- albedo , shading , normal -RRB- of scenes", "start": 48, "end": 124}}, {"head": {"text": "multiple view acquisition", "start": 130, "end": 155}, "tail": {"text": "intrinsic texture properties -LRB- albedo , shading , normal -RRB- of scenes", "start": 48, "end": 124}}], "feature of": [{"head": {"text": "unknown illumination conditions", "start": 162, "end": 193}, "tail": {"text": "multiple view acquisition", "start": 130, "end": 155}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Unlike previous video relighting methods , the approach does not assume regions of uniform albedo , which makes it applicable to richly textured scenes .", "output": {"relations": {"compare": [{"head": {"text": "approach", "start": 47, "end": 55}, "tail": {"text": "video relighting methods", "start": 16, "end": 40}}], "used for": [{"head": {"text": "it", "start": 112, "end": 114}, "tail": {"text": "richly textured scenes", "start": 129, "end": 151}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show that intrinsic image methods can be used to refine an initial , low-frequency shading estimate based on a global lighting reconstruction from an original texture and coarse scene geometry in order to resolve the inherent global ambiguity in shading .", "output": {"relations": {"used for": [{"head": {"text": "intrinsic image methods", "start": 13, "end": 36}, "tail": {"text": "initial , low-frequency shading estimate", "start": 62, "end": 102}}, {"head": {"text": "initial , low-frequency shading estimate", "start": 62, "end": 102}, "tail": {"text": "inherent global ambiguity in shading", "start": 220, "end": 256}}, {"head": {"text": "global lighting reconstruction", "start": 114, "end": 144}, "tail": {"text": "intrinsic image methods", "start": 13, "end": 36}}], "feature of": [{"head": {"text": "texture and coarse scene geometry", "start": 162, "end": 195}, "tail": {"text": "global lighting reconstruction", "start": 114, "end": 144}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The method is applied to relight-ing of free-viewpoint rendering from multiple view video capture .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 4, "end": 10}, "tail": {"text": "relight-ing of free-viewpoint rendering", "start": 25, "end": 64}}, {"head": {"text": "multiple view video capture", "start": 70, "end": 97}, "tail": {"text": "relight-ing of free-viewpoint rendering", "start": 25, "end": 64}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This demonstrates relighting with reproduction of fine surface detail .", "output": {"relations": {"feature of": [{"head": {"text": "reproduction of fine surface detail", "start": 34, "end": 69}, "tail": {"text": "relighting", "start": 18, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Following recent developments in the automatic evaluation of machine translation and document summarization , we present a similar approach , implemented in a measure called POURPRE , for automatically evaluating answers to definition questions .", "output": {"relations": {"evaluate for": [{"head": {"text": "automatic evaluation", "start": 37, "end": 57}, "tail": {"text": "machine translation", "start": 61, "end": 80}}, {"head": {"text": "automatic evaluation", "start": 37, "end": 57}, "tail": {"text": "document summarization", "start": 85, "end": 107}}], "conjunction": [{"head": {"text": "machine translation", "start": 61, "end": 80}, "tail": {"text": "document summarization", "start": 85, "end": 107}}], "used for": [{"head": {"text": "measure", "start": 159, "end": 166}, "tail": {"text": "approach", "start": 131, "end": 139}}, {"head": {"text": "measure", "start": 159, "end": 166}, "tail": {"text": "automatically evaluating answers to definition questions", "start": 188, "end": 244}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments with the TREC 2003 and TREC 2004 QA tracks indicate that rankings produced by our metric correlate highly with official rankings , and that POURPRE outperforms direct application of existing metrics .", "output": {"relations": {"evaluate for": [{"head": {"text": "TREC 2003 and TREC 2004 QA tracks", "start": 21, "end": 54}, "tail": {"text": "metric", "start": 94, "end": 100}}, {"head": {"text": "TREC 2003 and TREC 2004 QA tracks", "start": 21, "end": 54}, "tail": {"text": "POURPRE", "start": 152, "end": 159}}, {"head": {"text": "TREC 2003 and TREC 2004 QA tracks", "start": 21, "end": 54}, "tail": {"text": "metrics", "start": 203, "end": 210}}], "used for": [{"head": {"text": "metric", "start": 94, "end": 100}, "tail": {"text": "rankings", "start": 69, "end": 77}}], "compare": [{"head": {"text": "POURPRE", "start": 152, "end": 159}, "tail": {"text": "metrics", "start": 203, "end": 210}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach .", "output": {"relations": {"used for": [{"head": {"text": "Automatic Speech Recognition technology", "start": 19, "end": 58}, "tail": {"text": "dialog systems", "start": 99, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The issue of system response to users has been extensively studied by the natural language generation community , though rarely in the context of dialog systems .", "output": {"relations": {"part of": [{"head": {"text": "system response", "start": 13, "end": 28}, "tail": {"text": "natural language generation community", "start": 74, "end": 111}}], "compare": [{"head": {"text": "natural language generation community", "start": 74, "end": 111}, "tail": {"text": "dialog systems", "start": 146, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how research in generation can be adapted to dialog systems , and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques .", "output": {"relations": {"used for": [{"head": {"text": "generation", "start": 24, "end": 34}, "tail": {"text": "dialog systems", "start": 53, "end": 67}}, {"head": {"text": "machine learning techniques", "start": 173, "end": 200}, "tail": {"text": "hand-crafting knowledge-based generation systems", "start": 95, "end": 143}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a tool , called ILIMP , which takes as input a raw text in French and produces as output the same text in which every occurrence of the pronoun il is tagged either with tag -LSB- ANA -RSB- for anaphoric or -LSB- IMP -RSB- for impersonal or expletive .", "output": {"relations": {"used for": [{"head": {"text": "raw text in French", "start": 58, "end": 76}, "tail": {"text": "tool", "start": 13, "end": 17}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This tool is therefore designed to distinguish between the anaphoric occurrences of il , for which an anaphora resolution system has to look for an antecedent , and the expletive occurrences of this pronoun , for which it does not make sense to look for an antecedent .", "output": {"relations": {"used for": [{"head": {"text": "tool", "start": 5, "end": 9}, "tail": {"text": "anaphoric occurrences of il", "start": 59, "end": 86}}, {"head": {"text": "anaphora resolution system", "start": 102, "end": 128}, "tail": {"text": "anaphoric occurrences of il", "start": 59, "end": 86}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The precision rate for ILIMP is 97,5 % .", "output": {"relations": {"evaluate for": [{"head": {"text": "precision rate", "start": 4, "end": 18}, "tail": {"text": "ILIMP", "start": 23, "end": 28}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Other tasks using the method developed for ILIMP are described briefly , as well as the use of ILIMP in a modular syntactic analysis system .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "tasks", "start": 6, "end": 11}}, {"head": {"text": "method", "start": 22, "end": 28}, "tail": {"text": "ILIMP", "start": 43, "end": 48}}, {"head": {"text": "ILIMP", "start": 43, "end": 48}, "tail": {"text": "modular syntactic analysis system", "start": 106, "end": 139}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Little is thus known about the robustness of speech cues in the wild .", "output": {"relations": {"feature of": [{"head": {"text": "speech cues", "start": 45, "end": 56}, "tail": {"text": "robustness", "start": 31, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This study compares the effect of noise and reverberation on depression prediction using 1 -RRB- standard mel-frequency cepstral coefficients -LRB- MFCCs -RRB- , and 2 -RRB- features designed for noise robustness , damped oscillator cepstral coefficients -LRB- DOCCs -RRB- .", "output": {"relations": {"conjunction": [{"head": {"text": "noise", "start": 34, "end": 39}, "tail": {"text": "reverberation", "start": 44, "end": 57}}, {"head": {"text": "mel-frequency cepstral coefficients -LRB- MFCCs -RRB-", "start": 106, "end": 159}, "tail": {"text": "features", "start": 174, "end": 182}}, {"head": {"text": "features", "start": 174, "end": 182}, "tail": {"text": "damped oscillator cepstral coefficients -LRB- DOCCs -RRB-", "start": 215, "end": 272}}], "feature of": [{"head": {"text": "noise", "start": 34, "end": 39}, "tail": {"text": "depression prediction", "start": 61, "end": 82}}, {"head": {"text": "reverberation", "start": 44, "end": 57}, "tail": {"text": "depression prediction", "start": 61, "end": 82}}], "used for": [{"head": {"text": "mel-frequency cepstral coefficients -LRB- MFCCs -RRB-", "start": 106, "end": 159}, "tail": {"text": "depression prediction", "start": 61, "end": 82}}, {"head": {"text": "features", "start": 174, "end": 182}, "tail": {"text": "noise robustness", "start": 196, "end": 212}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Results using additive noise and reverberation reveal a consistent pattern of findings for multiple evaluation metrics under both matched and mismatched conditions .", "output": {"relations": {"conjunction": [{"head": {"text": "additive noise", "start": 14, "end": 28}, "tail": {"text": "reverberation", "start": 33, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First and most notably : standard MFCC features suffer dramatically under test/train mismatch for both noise and reverberation ; DOCC features are far more robust .", "output": {"relations": {"conjunction": [{"head": {"text": "noise", "start": 103, "end": 108}, "tail": {"text": "reverberation", "start": 113, "end": 126}}], "compare": [{"head": {"text": "DOCC features", "start": 129, "end": 142}, "tail": {"text": "MFCC features", "start": 34, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Third , artificial neural networks tend to outperform support vector regression .", "output": {"relations": {"compare": [{"head": {"text": "artificial neural networks", "start": 8, "end": 34}, "tail": {"text": "support vector regression", "start": 54, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Fourth , spontaneous speech appears to offer better robustness than read speech .", "output": {"relations": {"compare": [{"head": {"text": "spontaneous speech", "start": 9, "end": 27}, "tail": {"text": "read speech", "start": 68, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , a cross-corpus -LRB- and cross-language -RRB- experiment reveals better noise and reverberation robustness for DOCCs than for MFCCs .", "output": {"relations": {"evaluate for": [{"head": {"text": "cross-corpus -LRB- and cross-language -RRB- experiment", "start": 12, "end": 66}, "tail": {"text": "DOCCs", "start": 121, "end": 126}}, {"head": {"text": "cross-corpus -LRB- and cross-language -RRB- experiment", "start": 12, "end": 66}, "tail": {"text": "MFCCs", "start": 136, "end": 141}}, {"head": {"text": "noise and reverberation robustness", "start": 82, "end": 116}, "tail": {"text": "DOCCs", "start": 121, "end": 126}}, {"head": {"text": "noise and reverberation robustness", "start": 82, "end": 116}, "tail": {"text": "MFCCs", "start": 136, "end": 141}}], "compare": [{"head": {"text": "DOCCs", "start": 121, "end": 126}, "tail": {"text": "MFCCs", "start": 136, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes document oriented preference sets -LRB- DoPS -RRB- for the disambiguation of the dependency structure of sentences .", "output": {"relations": {"used for": [{"head": {"text": "document oriented preference sets -LRB- DoPS -RRB-", "start": 20, "end": 70}, "tail": {"text": "disambiguation of the dependency structure", "start": 79, "end": 121}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Sentence ambiguities can be resolved by using domain targeted preference knowledge without using complicated large knowledgebases .", "output": {"relations": {"used for": [{"head": {"text": "domain targeted preference knowledge", "start": 46, "end": 82}, "tail": {"text": "Sentence ambiguities", "start": 0, "end": 20}}], "compare": [{"head": {"text": "domain targeted preference knowledge", "start": 46, "end": 82}, "tail": {"text": "knowledgebases", "start": 115, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Implementation and empirical results are described for the the analysis of dependency structures of Japanese patent claim sentences .", "output": {"relations": {"feature of": [{"head": {"text": "dependency structures", "start": 75, "end": 96}, "tail": {"text": "Japanese patent claim sentences", "start": 100, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Multimodal interfaces require effective parsing and understanding of utterances whose content is distributed across multiple input modes .", "output": {"relations": {"used for": [{"head": {"text": "parsing", "start": 40, "end": 47}, "tail": {"text": "Multimodal interfaces", "start": 0, "end": 21}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Johnston 1998 presents an approach in which strategies for multimodal integration are stated declaratively using a unification-based grammar that is used by a multidimensional chart parser to compose inputs .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 26, "end": 34}, "tail": {"text": "multimodal integration", "start": 59, "end": 81}}, {"head": {"text": "unification-based grammar", "start": 115, "end": 140}, "tail": {"text": "multimodal integration", "start": 59, "end": 81}}, {"head": {"text": "unification-based grammar", "start": 115, "end": 140}, "tail": {"text": "multidimensional chart parser", "start": 159, "end": 188}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present an alternative approach in which multimodal parsing and understanding are achieved using a weighted finite-state device which takes speech and gesture streams as inputs and outputs their joint interpretation .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 42, "end": 50}, "tail": {"text": "multimodal parsing and understanding", "start": 60, "end": 96}}, {"head": {"text": "weighted finite-state device", "start": 118, "end": 146}, "tail": {"text": "multimodal parsing and understanding", "start": 60, "end": 96}}, {"head": {"text": "speech and gesture streams", "start": 159, "end": 185}, "tail": {"text": "weighted finite-state device", "start": 118, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This approach is significantly more efficient , enables tight-coupling of multimodal understanding with speech recognition , and provides a general probabilistic framework for multimodal ambiguity resolution .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 5, "end": 13}, "tail": {"text": "multimodal ambiguity resolution", "start": 176, "end": 207}}], "conjunction": [{"head": {"text": "speech recognition", "start": 104, "end": 122}, "tail": {"text": "multimodal understanding", "start": 74, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recently , we initiated a project to develop a phonetically-based spoken language understanding system called SUMMIT .", "output": {"relations": {"hyponym of": [{"head": {"text": "SUMMIT", "start": 110, "end": 116}, "tail": {"text": "phonetically-based spoken language understanding system", "start": 47, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering , our approach attempts to express the speech knowledge within a formal framework using well-defined mathematical tools .", "output": {"relations": {"used for": [{"head": {"text": "knowledge engineering", "start": 108, "end": 129}, "tail": {"text": "heuristic rules", "start": 57, "end": 72}}, {"head": {"text": "approach", "start": 136, "end": 144}, "tail": {"text": "speech knowledge", "start": 169, "end": 185}}, {"head": {"text": "mathematical tools", "start": 231, "end": 249}, "tail": {"text": "speech knowledge", "start": 169, "end": 185}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In our system , features and decision strategies are discovered and trained automatically , using a large body of speech data .", "output": {"relations": {"conjunction": [{"head": {"text": "features", "start": 16, "end": 24}, "tail": {"text": "decision strategies", "start": 29, "end": 48}}], "used for": [{"head": {"text": "speech data", "start": 114, "end": 125}, "tail": {"text": "decision strategies", "start": 29, "end": 48}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes an implemented program that takes a tagged text corpus and generates a partial list of the subcategorization frames in which each verb occurs .", "output": {"relations": {"evaluate for": [{"head": {"text": "tagged text corpus", "start": 57, "end": 75}, "tail": {"text": "program", "start": 36, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a method for estimating the relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras from observing a moving object .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 13, "end": 19}, "tail": {"text": "relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras", "start": 39, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We show how to tackle the problem of missing point correspondences heavily required by SfM pipelines and how to go beyond this basic paradigm .", "output": {"relations": {"used for": [{"head": {"text": "SfM pipelines", "start": 87, "end": 100}, "tail": {"text": "missing point correspondences", "start": 37, "end": 66}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We relax the non-linear nature of the problem by accepting two assumptions which surveillance scenarios offer , ie .", "output": {"relations": {"feature of": [{"head": {"text": "non-linear nature", "start": 13, "end": 30}, "tail": {"text": "problem", "start": 38, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By those assumptions we cast the problem as a Quadratic Eigenvalue Problem offering an elegant way of treating nonlinear monomials and delivering a quasi closed-form solution as a reliable starting point for a further bundle adjustment .", "output": {"relations": {"used for": [{"head": {"text": "Quadratic Eigenvalue Problem", "start": 46, "end": 74}, "tail": {"text": "problem", "start": 33, "end": 40}}, {"head": {"text": "Quadratic Eigenvalue Problem", "start": 46, "end": 74}, "tail": {"text": "nonlinear monomials", "start": 111, "end": 130}}, {"head": {"text": "Quadratic Eigenvalue Problem", "start": 46, "end": 74}, "tail": {"text": "quasi closed-form solution", "start": 148, "end": 174}}, {"head": {"text": "quasi closed-form solution", "start": 148, "end": 174}, "tail": {"text": "bundle adjustment", "start": 218, "end": 235}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We are the first to bring the closed form solution to such a very practical problem arising in video surveillance .", "output": {"relations": {"used for": [{"head": {"text": "closed form solution", "start": 30, "end": 50}, "tail": {"text": "problem", "start": 76, "end": 83}}], "feature of": [{"head": {"text": "video surveillance", "start": 95, "end": 113}, "tail": {"text": "problem", "start": 76, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a human action recognition system suitable for embedded computer vision applications in security systems , human-computer interaction and intelligent environments .", "output": {"relations": {"used for": [{"head": {"text": "human action recognition system", "start": 29, "end": 60}, "tail": {"text": "embedded computer vision applications", "start": 74, "end": 111}}, {"head": {"text": "embedded computer vision applications", "start": 74, "end": 111}, "tail": {"text": "security systems", "start": 115, "end": 131}}, {"head": {"text": "embedded computer vision applications", "start": 74, "end": 111}, "tail": {"text": "human-computer interaction", "start": 134, "end": 160}}, {"head": {"text": "embedded computer vision applications", "start": 74, "end": 111}, "tail": {"text": "intelligent environments", "start": 165, "end": 189}}], "conjunction": [{"head": {"text": "security systems", "start": 115, "end": 131}, "tail": {"text": "human-computer interaction", "start": 134, "end": 160}}, {"head": {"text": "human-computer interaction", "start": 134, "end": 160}, "tail": {"text": "intelligent environments", "start": 165, "end": 189}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our system is suitable for embedded computer vision application based on three reasons .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "embedded computer vision application", "start": 27, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Firstly , the system was based on a linear Support Vector Machine -LRB- SVM -RRB- classifier where classification progress can be implemented easily and quickly in embedded hardware .", "output": {"relations": {"used for": [{"head": {"text": "linear Support Vector Machine -LRB- SVM -RRB- classifier", "start": 36, "end": 92}, "tail": {"text": "system", "start": 14, "end": 20}}, {"head": {"text": "embedded hardware", "start": 164, "end": 181}, "tail": {"text": "classification progress", "start": 99, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Secondly , we use compacted motion features easily obtained from videos .", "output": {"relations": {"used for": [{"head": {"text": "videos", "start": 65, "end": 71}, "tail": {"text": "compacted motion features", "start": 18, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We address the limitations of the well known Motion History Image -LRB- MHI -RRB- and propose a new Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature to represent the motion information .", "output": {"relations": {"used for": [{"head": {"text": "Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature", "start": 100, "end": 162}, "tail": {"text": "motion information", "start": 180, "end": 198}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "HMHH not only provides rich motion information , but also remains computationally inexpensive .", "output": {"relations": {"used for": [{"head": {"text": "HMHH", "start": 0, "end": 4}, "tail": {"text": "rich motion information", "start": 23, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers .", "output": {"relations": {"conjunction": [{"head": {"text": "MHI", "start": 21, "end": 24}, "tail": {"text": "HMHH", "start": 29, "end": 33}}], "used for": [{"head": {"text": "MHI", "start": 21, "end": 24}, "tail": {"text": "low dimension feature vector", "start": 57, "end": 85}}, {"head": {"text": "HMHH", "start": 29, "end": 33}, "tail": {"text": "low dimension feature vector", "start": 57, "end": 85}}, {"head": {"text": "low dimension feature vector", "start": 57, "end": 85}, "tail": {"text": "SVM classifiers", "start": 104, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results show that our system achieves significant improvement on the recognition performance .", "output": {"relations": {"evaluate for": [{"head": {"text": "recognition", "start": 82, "end": 93}, "tail": {"text": "system", "start": 35, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources .", "output": {"relations": {"used for": [{"head": {"text": "uniform processing", "start": 82, "end": 100}, "tail": {"text": "model of grammatical processing", "start": 33, "end": 64}}, {"head": {"text": "knowledge sources", "start": 105, "end": 122}, "tail": {"text": "model of grammatical processing", "start": 33, "end": 64}}], "conjunction": [{"head": {"text": "knowledge sources", "start": 105, "end": 122}, "tail": {"text": "uniform processing", "start": 82, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process .", "output": {"relations": {"conjunction": [{"head": {"text": "parsing", "start": 42, "end": 49}, "tail": {"text": "generation", "start": 54, "end": 64}}], "hyponym of": [{"head": {"text": "parsing", "start": 42, "end": 49}, "tail": {"text": "tasks", "start": 93, "end": 98}}, {"head": {"text": "generation", "start": 54, "end": 64}, "tail": {"text": "tasks", "start": 93, "end": 98}}], "used for": [{"head": {"text": "parametrized deduction process", "start": 121, "end": 151}, "tail": {"text": "tasks", "start": 93, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Link detection has been regarded as a core technology for the Topic Detection and Tracking tasks of new event detection .", "output": {"relations": {"used for": [{"head": {"text": "Link detection", "start": 0, "end": 14}, "tail": {"text": "Topic Detection and Tracking tasks of new event detection", "start": 62, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both systems .", "output": {"relations": {"conjunction": [{"head": {"text": "story link detection", "start": 27, "end": 47}, "tail": {"text": "new event detection", "start": 52, "end": 71}}, {"head": {"text": "precision", "start": 135, "end": 144}, "tail": {"text": "recall", "start": 149, "end": 155}}], "hyponym of": [{"head": {"text": "story link detection", "start": 27, "end": 47}, "tail": {"text": "systems", "start": 164, "end": 171}}, {"head": {"text": "new event detection", "start": 52, "end": 71}, "tail": {"text": "systems", "start": 164, "end": 171}}], "used for": [{"head": {"text": "information retrieval task", "start": 75, "end": 101}, "tail": {"text": "story link detection", "start": 27, "end": 47}}, {"head": {"text": "information retrieval task", "start": 75, "end": 101}, "tail": {"text": "new event detection", "start": 52, "end": 71}}], "evaluate for": [{"head": {"text": "precision", "start": 135, "end": 144}, "tail": {"text": "systems", "start": 164, "end": 171}}, {"head": {"text": "recall", "start": 149, "end": 155}, "tail": {"text": "systems", "start": 164, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Motivated by these arguments , we introduce a number of new performance enhancing techniques including part of speech tagging , new similarity measures and expanded stop lists .", "output": {"relations": {"part of": [{"head": {"text": "part of speech tagging", "start": 103, "end": 125}, "tail": {"text": "performance enhancing techniques", "start": 60, "end": 92}}, {"head": {"text": "similarity measures", "start": 132, "end": 151}, "tail": {"text": "performance enhancing techniques", "start": 60, "end": 92}}, {"head": {"text": "expanded stop lists", "start": 156, "end": 175}, "tail": {"text": "performance enhancing techniques", "start": 60, "end": 92}}], "conjunction": [{"head": {"text": "part of speech tagging", "start": 103, "end": 125}, "tail": {"text": "similarity measures", "start": 132, "end": 151}}, {"head": {"text": "similarity measures", "start": 132, "end": 151}, "tail": {"text": "expanded stop lists", "start": 156, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We attempt to understand visual classification in humans using both psy-chophysical and machine learning techniques .", "output": {"relations": {"used for": [{"head": {"text": "psy-chophysical and machine learning techniques", "start": 68, "end": 115}, "tail": {"text": "visual classification", "start": 25, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Frontal views of human faces were used for a gender classification task .", "output": {"relations": {"used for": [{"head": {"text": "Frontal views of human faces", "start": 0, "end": 28}, "tail": {"text": "gender classification task", "start": 45, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and flowfield representation of the faces .", "output": {"relations": {"used for": [{"head": {"text": "hyperplane learning algorithms", "start": 8, "end": 38}, "tail": {"text": "classification task", "start": 61, "end": 80}}, {"head": {"text": "Principal Components of the texture", "start": 91, "end": 126}, "tail": {"text": "hyperplane learning algorithms", "start": 8, "end": 38}}, {"head": {"text": "flowfield representation of the faces", "start": 131, "end": 168}, "tail": {"text": "hyperplane learning algorithms", "start": 8, "end": 38}}], "conjunction": [{"head": {"text": "flowfield representation of the faces", "start": 131, "end": 168}, "tail": {"text": "Principal Components of the texture", "start": 91, "end": 126}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The classification performance of the learning algorithms was estimated using the face database with the true gender of the faces as labels , and also with the gender estimated by the subjects .", "output": {"relations": {"used for": [{"head": {"text": "learning algorithms", "start": 38, "end": 57}, "tail": {"text": "classification", "start": 4, "end": 18}}], "evaluate for": [{"head": {"text": "face database", "start": 82, "end": 95}, "tail": {"text": "learning algorithms", "start": 38, "end": 57}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our results suggest that human classification can be modeled by some hyperplane algorithms in the feature space we used .", "output": {"relations": {"used for": [{"head": {"text": "hyperplane algorithms", "start": 69, "end": 90}, "tail": {"text": "human classification", "start": 25, "end": 45}}], "feature of": [{"head": {"text": "feature space", "start": 98, "end": 111}, "tail": {"text": "hyperplane algorithms", "start": 69, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For classification , the brain needs more processing for stimuli close to that hyperplane than for those further away .", "output": {"relations": {"compare": [{"head": {"text": "hyperplane", "start": 79, "end": 89}, "tail": {"text": "those", "start": 99, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system for Dutch which combines statistical classification -LRB- maximum entropy -RRB- with linguistic information .", "output": {"relations": {"used for": [{"head": {"text": "corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system", "start": 29, "end": 101}, "tail": {"text": "Dutch", "start": 106, "end": 111}}], "part of": [{"head": {"text": "statistical classification", "start": 127, "end": 153}, "tail": {"text": "corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system", "start": 29, "end": 101}}, {"head": {"text": "maximum entropy", "start": 160, "end": 175}, "tail": {"text": "corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system", "start": 29, "end": 101}}, {"head": {"text": "linguistic information", "start": 187, "end": 209}, "tail": {"text": "corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system", "start": 29, "end": 101}}], "conjunction": [{"head": {"text": "linguistic information", "start": 187, "end": 209}, "tail": {"text": "maximum entropy", "start": 160, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Instead of building individual classifiers per ambiguous wordform , we introduce a lemma-based approach .", "output": {"relations": {"compare": [{"head": {"text": "classifiers", "start": 31, "end": 42}, "tail": {"text": "lemma-based approach", "start": 83, "end": 103}}], "used for": [{"head": {"text": "ambiguous wordform", "start": 47, "end": 65}, "tail": {"text": "classifiers", "start": 31, "end": 42}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in one classifier , therefore augmenting the training material available to the algorithm .", "output": {"relations": {"feature of": [{"head": {"text": "inflected forms", "start": 59, "end": 74}, "tail": {"text": "ambiguous word", "start": 81, "end": 95}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Testing the lemma-based model on the Dutch Senseval-2 test data , we achieve a significant increase in accuracy over the wordform model .", "output": {"relations": {"compare": [{"head": {"text": "lemma-based model", "start": 12, "end": 29}, "tail": {"text": "wordform model", "start": 121, "end": 135}}], "evaluate for": [{"head": {"text": "Dutch Senseval-2 test data", "start": 37, "end": 63}, "tail": {"text": "lemma-based model", "start": 12, "end": 29}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose an exact , general and efficient coarse-to-fine energy minimization strategy for semantic video segmenta-tion .", "output": {"relations": {"used for": [{"head": {"text": "coarse-to-fine energy minimization strategy", "start": 44, "end": 87}, "tail": {"text": "semantic video segmenta-tion", "start": 92, "end": 120}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs .", "output": {"relations": {"used for": [{"head": {"text": "hierarchical abstraction of the supervoxel graph", "start": 27, "end": 75}, "tail": {"text": "strategy", "start": 4, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is general , i.e. , it can be used to minimize any energy function -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing energy minimization algorithm -LRB- e.g. , graph cuts and belief propagation -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "it", "start": 23, "end": 25}, "tail": {"text": "energy function", "start": 54, "end": 69}}, {"head": {"text": "energy minimization algorithm", "start": 149, "end": 178}, "tail": {"text": "energy function", "start": 54, "end": 69}}], "conjunction": [{"head": {"text": "it", "start": 23, "end": 25}, "tail": {"text": "energy minimization algorithm", "start": 149, "end": 178}}, {"head": {"text": "graph cuts", "start": 192, "end": 202}, "tail": {"text": "belief propagation", "start": 207, "end": 225}}], "hyponym of": [{"head": {"text": "graph cuts", "start": 192, "end": 202}, "tail": {"text": "energy minimization algorithm", "start": 149, "end": 178}}, {"head": {"text": "belief propagation", "start": 207, "end": 225}, "tail": {"text": "energy minimization algorithm", "start": 149, "end": 178}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It also gives significant speedups in inference for several datasets with varying degrees of spatio-temporal continuity .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "inference", "start": 38, "end": 47}}], "evaluate for": [{"head": {"text": "datasets", "start": 60, "end": 68}, "tail": {"text": "It", "start": 0, "end": 2}}], "feature of": [{"head": {"text": "spatio-temporal continuity", "start": 93, "end": 119}, "tail": {"text": "datasets", "start": 60, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also discuss the strengths and weaknesses of our strategy relative to existing hierarchical approaches , and the kinds of image and video data that provide the best speedups .", "output": {"relations": {"compare": [{"head": {"text": "strategy", "start": 52, "end": 60}, "tail": {"text": "hierarchical approaches", "start": 82, "end": 105}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Motivated by the success of ensemble methods in machine learning and other areas of natural language processing , we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora .", "output": {"relations": {"used for": [{"head": {"text": "ensemble methods", "start": 28, "end": 44}, "tail": {"text": "machine learning", "start": 48, "end": 64}}, {"head": {"text": "ensemble methods", "start": 28, "end": 44}, "tail": {"text": "natural language processing", "start": 84, "end": 111}}, {"head": {"text": "multi-strategy and multi-source approach", "start": 129, "end": 169}, "tail": {"text": "question answering", "start": 173, "end": 191}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The answering agents adopt fundamentally different strategies , one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques .", "output": {"relations": {"used for": [{"head": {"text": "strategies", "start": 51, "end": 61}, "tail": {"text": "answering agents", "start": 4, "end": 20}}, {"head": {"text": "knowledge-based mechanisms", "start": 88, "end": 114}, "tail": {"text": "one", "start": 64, "end": 67}}, {"head": {"text": "statistical techniques", "start": 138, "end": 160}, "tail": {"text": "other", "start": 123, "end": 128}}], "hyponym of": [{"head": {"text": "one", "start": 64, "end": 67}, "tail": {"text": "strategies", "start": 51, "end": 61}}, {"head": {"text": "other", "start": 123, "end": 128}, "tail": {"text": "strategies", "start": 51, "end": 61}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present our multi-level answer resolution algorithm that combines results from the answering agents at the question , passage , and/or answer levels .", "output": {"relations": {"used for": [{"head": {"text": "answering agents", "start": 86, "end": 102}, "tail": {"text": "multi-level answer resolution algorithm", "start": 15, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0 % relative improvement over our baseline system in the number of questions correctly answered , and a 32.8 % improvement according to the average precision metric .", "output": {"relations": {"compare": [{"head": {"text": "answer resolution algorithm", "start": 48, "end": 75}, "tail": {"text": "baseline system", "start": 120, "end": 135}}], "evaluate for": [{"head": {"text": "average precision metric", "start": 226, "end": 250}, "tail": {"text": "answer resolution algorithm", "start": 48, "end": 75}}, {"head": {"text": "average precision metric", "start": 226, "end": 250}, "tail": {"text": "baseline system", "start": 120, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Word Identification has been an important and active issue in Chinese Natural Language Processing .", "output": {"relations": {"hyponym of": [{"head": {"text": "Word Identification", "start": 0, "end": 19}, "tail": {"text": "Chinese Natural Language Processing", "start": 62, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , a new mechanism , based on the concept of sublanguage , is proposed for identifying unknown words , especially personal names , in Chinese newspapers .", "output": {"relations": {"used for": [{"head": {"text": "mechanism", "start": 22, "end": 31}, "tail": {"text": "unknown words", "start": 100, "end": 113}}, {"head": {"text": "sublanguage", "start": 58, "end": 69}, "tail": {"text": "mechanism", "start": 22, "end": 31}}, {"head": {"text": "Chinese newspapers", "start": 147, "end": 165}, "tail": {"text": "mechanism", "start": 22, "end": 31}}], "hyponym of": [{"head": {"text": "personal names", "start": 127, "end": 141}, "tail": {"text": "unknown words", "start": 100, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed mechanism includes title-driven name recognition , adaptive dynamic word formation , identification of 2-character and 3-character Chinese names without title .", "output": {"relations": {"part of": [{"head": {"text": "title-driven name recognition", "start": 32, "end": 61}, "tail": {"text": "mechanism", "start": 13, "end": 22}}, {"head": {"text": "adaptive dynamic word formation", "start": 64, "end": 95}, "tail": {"text": "mechanism", "start": 13, "end": 22}}, {"head": {"text": "identification of 2-character and 3-character Chinese names without title", "start": 98, "end": 171}, "tail": {"text": "mechanism", "start": 13, "end": 22}}], "conjunction": [{"head": {"text": "title-driven name recognition", "start": 32, "end": 61}, "tail": {"text": "adaptive dynamic word formation", "start": 64, "end": 95}}, {"head": {"text": "adaptive dynamic word formation", "start": 64, "end": 95}, "tail": {"text": "identification of 2-character and 3-character Chinese names without title", "start": 98, "end": 171}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This report describes Paul , a computer text generation system designed to create cohesive text through the use of lexical substitutions .", "output": {"relations": {"hyponym of": [{"head": {"text": "Paul", "start": 22, "end": 26}, "tail": {"text": "computer text generation system", "start": 31, "end": 62}}], "used for": [{"head": {"text": "computer text generation system", "start": 31, "end": 62}, "tail": {"text": "cohesive text", "start": 82, "end": 95}}, {"head": {"text": "lexical substitutions", "start": 115, "end": 136}, "tail": {"text": "Paul", "start": 22, "end": 26}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , this system is designed to deterministically choose between pronominalization , superordinate substitution , and definite noun phrase reiteration .", "output": {"relations": {"compare": [{"head": {"text": "pronominalization", "start": 75, "end": 92}, "tail": {"text": "superordinate substitution", "start": 95, "end": 121}}, {"head": {"text": "superordinate substitution", "start": 95, "end": 121}, "tail": {"text": "definite noun phrase reiteration", "start": 128, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system identifies a strength of antecedence recovery for each of the lexical substitutions .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "antecedence recovery", "start": 36, "end": 56}}, {"head": {"text": "antecedence recovery", "start": 36, "end": 56}, "tail": {"text": "lexical substitutions", "start": 73, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It describes the automated training and evaluation of an Optimal Position Policy , a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 85, "end": 91}, "tail": {"text": "positions of topic-bearing sentences", "start": 115, "end": 151}}, {"head": {"text": "genre-specific regularities of discourse structure", "start": 161, "end": 211}, "tail": {"text": "method", "start": 85, "end": 91}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This method can be used in applications such as information retrieval , routing , and text summarization .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 5, "end": 11}, "tail": {"text": "applications", "start": 27, "end": 39}}], "hyponym of": [{"head": {"text": "information retrieval", "start": 48, "end": 69}, "tail": {"text": "applications", "start": 27, "end": 39}}, {"head": {"text": "routing", "start": 72, "end": 79}, "tail": {"text": "applications", "start": 27, "end": 39}}, {"head": {"text": "text summarization", "start": 86, "end": 104}, "tail": {"text": "applications", "start": 27, "end": 39}}], "conjunction": [{"head": {"text": "information retrieval", "start": 48, "end": 69}, "tail": {"text": "routing", "start": 72, "end": 79}}, {"head": {"text": "routing", "start": 72, "end": 79}, "tail": {"text": "text summarization", "start": 86, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a general framework for online multiclass learning based on the notion of hypothesis sharing .", "output": {"relations": {"used for": [{"head": {"text": "framework", "start": 22, "end": 31}, "tail": {"text": "online multiclass learning", "start": 36, "end": 62}}, {"head": {"text": "notion of hypothesis sharing", "start": 76, "end": 104}, "tail": {"text": "framework", "start": 22, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis .", "output": {"relations": {"used for": [{"head": {"text": "multiclass Perceptron", "start": 18, "end": 39}, "tail": {"text": "framework", "start": 47, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 22, "end": 24}, "tail": {"text": "methods", "start": 70, "end": 77}}], "evaluate for": [{"head": {"text": "synthetic and natural datasets", "start": 86, "end": 116}, "tail": {"text": "it", "start": 22, "end": 24}}, {"head": {"text": "synthetic and natural datasets", "start": 86, "end": 116}, "tail": {"text": "methods", "start": 70, "end": 77}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions .", "output": {"relations": {"used for": [{"head": {"text": "supervised machine learning", "start": 21, "end": 48}, "tail": {"text": "statistical models of WH-questions", "start": 94, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These models , which are built from shallow linguistic features of questions , are employed to predict target variables which represent a user 's informational goals .", "output": {"relations": {"used for": [{"head": {"text": "shallow linguistic features of questions", "start": 36, "end": 76}, "tail": {"text": "models", "start": 6, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We argue in favor of the the use of labeled directed graph to represent various types of linguistic structures , and illustrate how this allows one to view NLP tasks as graph transformations .", "output": {"relations": {"used for": [{"head": {"text": "labeled directed graph", "start": 36, "end": 58}, "tail": {"text": "linguistic structures", "start": 89, "end": 110}}, {"head": {"text": "labeled directed graph", "start": 36, "end": 58}, "tail": {"text": "NLP tasks", "start": 156, "end": 165}}, {"head": {"text": "this", "start": 132, "end": 136}, "tail": {"text": "NLP tasks", "start": 156, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a general method for learning such transformations from an annotated corpus and describe experiments with two applications of the method : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 21, "end": 27}, "tail": {"text": "transformations", "start": 46, "end": 61}}, {"head": {"text": "annotated corpus", "start": 70, "end": 86}, "tail": {"text": "method", "start": 21, "end": 27}}, {"head": {"text": "method", "start": 21, "end": 27}, "tail": {"text": "applications", "start": 121, "end": 133}}, {"head": {"text": "Penn Treebank data", "start": 202, "end": 220}, "tail": {"text": "identification of non-local depenencies", "start": 150, "end": 189}}, {"head": {"text": "Proposition Bank data", "start": 266, "end": 287}, "tail": {"text": "semantic role labeling", "start": 231, "end": 253}}], "hyponym of": [{"head": {"text": "identification of non-local depenencies", "start": 150, "end": 189}, "tail": {"text": "applications", "start": 121, "end": 133}}, {"head": {"text": "semantic role labeling", "start": 231, "end": 253}, "tail": {"text": "applications", "start": 121, "end": 133}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a generative probabilistic model of natural language , which we call HBG , that takes advantage of detailed linguistic information to resolve ambiguity .", "output": {"relations": {"used for": [{"head": {"text": "linguistic information", "start": 120, "end": 142}, "tail": {"text": "ambiguity", "start": 154, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "HBG incorporates lexical , syntactic , semantic , and structural information from the parse tree into the disambiguation process in a novel way .", "output": {"relations": {"used for": [{"head": {"text": "HBG", "start": 0, "end": 3}, "tail": {"text": "disambiguation process", "start": 106, "end": 128}}, {"head": {"text": "lexical , syntactic , semantic , and structural information", "start": 17, "end": 76}, "tail": {"text": "HBG", "start": 0, "end": 3}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We use a corpus of bracketed sentences , called a Treebank , in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence .", "output": {"relations": {"conjunction": [{"head": {"text": "corpus of bracketed sentences", "start": 9, "end": 38}, "tail": {"text": "decision tree building", "start": 81, "end": 103}}], "used for": [{"head": {"text": "corpus of bracketed sentences", "start": 9, "end": 38}, "tail": {"text": "parse tree", "start": 143, "end": 153}}, {"head": {"text": "decision tree building", "start": 81, "end": 103}, "tail": {"text": "parse tree", "start": 143, "end": 153}}, {"head": {"text": "parse tree", "start": 143, "end": 153}, "tail": {"text": "parse", "start": 143, "end": 148}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse .", "output": {"relations": {"used for": [{"head": {"text": "grammar tailoring", "start": 57, "end": 74}, "tail": {"text": "parse", "start": 152, "end": 157}}, {"head": {"text": "linguistic introspection", "start": 89, "end": 113}, "tail": {"text": "grammar tailoring", "start": 57, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In head-to-head tests against one of the best existing robust probabilistic parsing models , which we call P-CFG , the HBG model significantly outperforms P-CFG , increasing the parsing accuracy rate from 60 % to 75 % , a 37 % reduction in error .", "output": {"relations": {"hyponym of": [{"head": {"text": "P-CFG", "start": 107, "end": 112}, "tail": {"text": "robust probabilistic parsing models", "start": 55, "end": 90}}], "compare": [{"head": {"text": "HBG model", "start": 119, "end": 128}, "tail": {"text": "P-CFG", "start": 107, "end": 112}}], "evaluate for": [{"head": {"text": "parsing accuracy rate", "start": 178, "end": 199}, "tail": {"text": "HBG model", "start": 119, "end": 128}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The framework of the analysis is model-theoretic semantics .", "output": {"relations": {"used for": [{"head": {"text": "model-theoretic semantics", "start": 33, "end": 58}, "tail": {"text": "analysis", "start": 21, "end": 29}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources .", "output": {"relations": {"used for": [{"head": {"text": "machine-readable resources", "start": 74, "end": 100}, "tail": {"text": "word-sense ambiguity", "start": 34, "end": 54}}, {"head": {"text": "machine-readable resources", "start": 74, "end": 100}, "tail": {"text": "construction of large-scale knowledge sources", "start": 109, "end": 154}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe two experiments : one which ignored word-sense distinctions , resulting in 6.3 % accuracy for semantic classification of verbs based on -LRB- Levin , 1993 -RRB- ; and one which exploited word-sense distinctions , resulting in 97.9 % accuracy .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 93, "end": 101}, "tail": {"text": "semantic classification", "start": 106, "end": 129}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These experiments were dual purpose : -LRB- 1 -RRB- to validate the central thesis of the work of -LRB- Levin , 1993 -RRB- , i.e. , that verb semantics and syntactic behavior are predictably related ; -LRB- 2 -RRB- to demonstrate that a 15-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses .", "output": {"relations": {"conjunction": [{"head": {"text": "verb semantics", "start": 137, "end": 151}, "tail": {"text": "syntactic behavior", "start": 156, "end": 174}}], "used for": [{"head": {"text": "syntactic cues", "start": 311, "end": 325}, "tail": {"text": "semantic information", "start": 285, "end": 305}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Finally , we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources .", "output": {"relations": {"used for": [{"head": {"text": "techniques", "start": 60, "end": 70}, "tail": {"text": "word senses", "start": 81, "end": 92}}, {"head": {"text": "online sources", "start": 116, "end": 130}, "tail": {"text": "techniques", "start": 60, "end": 70}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The TIPSTER Architecture has been designed to enable a variety of different text applications to use a set of common text processing modules .", "output": {"relations": {"used for": [{"head": {"text": "TIPSTER Architecture", "start": 4, "end": 24}, "tail": {"text": "text applications", "start": 76, "end": 93}}, {"head": {"text": "common text processing modules", "start": 110, "end": 140}, "tail": {"text": "text applications", "start": 76, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since user interfaces work best when customized for particular applications , it is appropriator that no particular user interface styles or conventions are described in the TIPSTER Architecture specification .", "output": {"relations": {"used for": [{"head": {"text": "user interfaces", "start": 6, "end": 21}, "tail": {"text": "applications", "start": 63, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , the Computing Research Laboratory -LRB- CRL -RRB- has constructed several TIPSTER applications that use a common set of configurable Graphical User Interface -LRB- GUI -RRB- functions .", "output": {"relations": {"used for": [{"head": {"text": "Graphical User Interface -LRB- GUI -RRB- functions", "start": 143, "end": 193}, "tail": {"text": "TIPSTER applications", "start": 84, "end": 104}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These GUIs were constructed using CRL 's TIPSTER User Interface Toolkit -LRB- TUIT -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "CRL 's TIPSTER User Interface Toolkit -LRB- TUIT -RRB-", "start": 34, "end": 88}, "tail": {"text": "GUIs", "start": 6, "end": 10}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "TUIT is a software library that can be used to construct multilingual TIPSTER user interfaces for a set of common user tasks .", "output": {"relations": {"hyponym of": [{"head": {"text": "TUIT", "start": 0, "end": 4}, "tail": {"text": "software library", "start": 10, "end": 26}}], "used for": [{"head": {"text": "TUIT", "start": 0, "end": 4}, "tail": {"text": "multilingual TIPSTER user interfaces", "start": 57, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "CRL developed TUIT to support their work to integrate TIPSTER modules for the 6 and 12 month TIPSTER II demonstrations as well as their Oleada and Temple demonstration projects .", "output": {"relations": {"used for": [{"head": {"text": "TUIT", "start": 14, "end": 18}, "tail": {"text": "TIPSTER modules", "start": 54, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While such decoding is an essential underpinning , much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication , such as robust communication procedures .", "output": {"relations": {"part of": [{"head": {"text": "non-literal aspects of communication", "start": 190, "end": 226}, "tail": {"text": "they", "start": 159, "end": 163}}], "hyponym of": [{"head": {"text": "robust communication procedures", "start": 237, "end": 268}, "tail": {"text": "non-literal aspects of communication", "start": 190, "end": 226}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper defends that view , but claims that direct imitation of human performance is not the best way to implement many of these non-literal aspects of communication ; that the new technology of powerful personal computers with integral graphics displays offers techniques superior to those of humans for these aspects , while still satisfying human communication needs .", "output": {"relations": {"part of": [{"head": {"text": "graphics displays", "start": 240, "end": 257}, "tail": {"text": "personal computers", "start": 207, "end": 225}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a framework in which Lagrangian Particle Dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities .", "output": {"relations": {"used for": [{"head": {"text": "Lagrangian Particle Dynamics", "start": 41, "end": 69}, "tail": {"text": "segmentation of high density crowd flows", "start": 86, "end": 126}}, {"head": {"text": "Lagrangian Particle Dynamics", "start": 41, "end": 69}, "tail": {"text": "detection of flow instabilities", "start": 131, "end": 162}}], "conjunction": [{"head": {"text": "segmentation of high density crowd flows", "start": 86, "end": 126}, "tail": {"text": "detection of flow instabilities", "start": 131, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For this purpose , a flow field generated by a moving crowd is treated as an aperiodic dynamical system .", "output": {"relations": {"used for": [{"head": {"text": "moving crowd", "start": 47, "end": 59}, "tail": {"text": "flow field", "start": 21, "end": 31}}, {"head": {"text": "aperiodic dynamical system", "start": 77, "end": 103}, "tail": {"text": "flow field", "start": 21, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A grid of particles is overlaid on the flow field , and is advected using a numerical integration scheme .", "output": {"relations": {"used for": [{"head": {"text": "grid of particles", "start": 2, "end": 19}, "tail": {"text": "flow field", "start": 39, "end": 49}}, {"head": {"text": "numerical integration scheme", "start": 76, "end": 104}, "tail": {"text": "grid of particles", "start": 2, "end": 19}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The evolution of particles through the flow is tracked using a Flow Map , whose spatial gradients are subsequently used to setup a Cauchy Green Deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration .", "output": {"relations": {"used for": [{"head": {"text": "Flow Map", "start": 63, "end": 71}, "tail": {"text": "evolution of particles", "start": 4, "end": 26}}, {"head": {"text": "spatial gradients", "start": 80, "end": 97}, "tail": {"text": "Cauchy Green Deformation tensor", "start": 131, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The maximum eigenvalue of the tensor is used to construct a Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field , which reveals the Lagrangian Coherent Structures -LRB- LCS -RRB- present in the underlying flow .", "output": {"relations": {"feature of": [{"head": {"text": "maximum eigenvalue", "start": 4, "end": 22}, "tail": {"text": "tensor", "start": 30, "end": 36}}], "used for": [{"head": {"text": "maximum eigenvalue", "start": 4, "end": 22}, "tail": {"text": "Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field", "start": 60, "end": 112}}, {"head": {"text": "Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field", "start": 60, "end": 112}, "tail": {"text": "Lagrangian Coherent Structures -LRB- LCS -RRB-", "start": 133, "end": 179}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework .", "output": {"relations": {"used for": [{"head": {"text": "LCS", "start": 4, "end": 7}, "tail": {"text": "boundaries of the flow segments", "start": 92, "end": 123}}, {"head": {"text": "normalized cuts framework", "start": 129, "end": 154}, "tail": {"text": "boundaries of the flow segments", "start": 92, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary .", "output": {"relations": {"conjunction": [{"head": {"text": "Google Video", "start": 72, "end": 84}, "tail": {"text": "National Geographic documentary", "start": 91, "end": 122}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Over the last decade , a variety of SMT algorithms have been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT .", "output": {"relations": {"evaluate for": [{"head": {"text": "computational complexity", "start": 124, "end": 148}, "tail": {"text": "problems", "start": 176, "end": 184}}], "part of": [{"head": {"text": "problems", "start": 176, "end": 184}, "tail": {"text": "SMT", "start": 36, "end": 39}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our work aims at providing useful insights into the the computational complexity of those problems .", "output": {"relations": {"evaluate for": [{"head": {"text": "computational complexity", "start": 56, "end": 80}, "tail": {"text": "problems", "start": 90, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We prove that while IBM Models 1-2 are conceptually and computationally simple , computations involving the higher -LRB- and more useful -RRB- models are hard .", "output": {"relations": {"compare": [{"head": {"text": "IBM Models 1-2", "start": 20, "end": 34}, "tail": {"text": "models", "start": 143, "end": 149}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Since it is unlikely that there exists a polynomial time solution for any of these hard problems -LRB- unless P = NP and P #P = P -RRB- , our results highlight and justify the need for developing polynomial time approximations for these computations .", "output": {"relations": {"used for": [{"head": {"text": "polynomial time solution", "start": 41, "end": 65}, "tail": {"text": "hard problems", "start": 83, "end": 96}}, {"head": {"text": "polynomial time approximations", "start": 196, "end": 226}, "tail": {"text": "computations", "start": 237, "end": 249}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Most state-of-the-art evaluation measures for machine translation assign high costs to movements of word blocks .", "output": {"relations": {"evaluate for": [{"head": {"text": "evaluation measures", "start": 22, "end": 41}, "tail": {"text": "machine translation", "start": 46, "end": 65}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we will present a new evaluation measure which explicitly models block reordering as an edit operation .", "output": {"relations": {"used for": [{"head": {"text": "evaluation measure", "start": 38, "end": 56}, "tail": {"text": "block reordering", "start": 81, "end": 97}}, {"head": {"text": "edit operation", "start": 104, "end": 118}, "tail": {"text": "block reordering", "start": 81, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our measure can be exactly calculated in quadratic time .", "output": {"relations": {"feature of": [{"head": {"text": "quadratic time", "start": 41, "end": 55}, "tail": {"text": "measure", "start": 4, "end": 11}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Furthermore , we will show how some evaluation measures can be improved by the introduction of word-dependent substitution costs .", "output": {"relations": {"used for": [{"head": {"text": "word-dependent substitution costs", "start": 95, "end": 128}, "tail": {"text": "evaluation measures", "start": 36, "end": 55}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The correlation of the new measure with human judgment has been investigated systematically on two different language pairs .", "output": {"relations": {"compare": [{"head": {"text": "measure", "start": 27, "end": 34}, "tail": {"text": "human judgment", "start": 40, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experimental results will show that it significantly outperforms state-of-the-art approaches in sentence-level correlation .", "output": {"relations": {"compare": [{"head": {"text": "it", "start": 40, "end": 42}, "tail": {"text": "approaches", "start": 86, "end": 96}}], "evaluate for": [{"head": {"text": "sentence-level correlation", "start": 100, "end": 126}, "tail": {"text": "it", "start": 40, "end": 42}}, {"head": {"text": "sentence-level correlation", "start": 100, "end": 126}, "tail": {"text": "approaches", "start": 86, "end": 96}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Results from experiments with word dependent substitution costs will demonstrate an additional increase of correlation between automatic evaluation measures and human judgment .", "output": {"relations": {"conjunction": [{"head": {"text": "automatic evaluation measures", "start": 127, "end": 156}, "tail": {"text": "human judgment", "start": 161, "end": 175}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Rete and Treat algorithms are considered the most efficient implementation techniques for Forward Chaining rule systems .", "output": {"relations": {"hyponym of": [{"head": {"text": "Rete and Treat algorithms", "start": 4, "end": 29}, "tail": {"text": "implementation techniques", "start": 64, "end": 89}}], "used for": [{"head": {"text": "Rete and Treat algorithms", "start": 4, "end": 29}, "tail": {"text": "Forward Chaining rule systems", "start": 94, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These algorithms support a language of limited expressive power .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 6, "end": 16}, "tail": {"text": "language of limited expressive power", "start": 27, "end": 63}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we show how to support full unification in these algorithms .", "output": {"relations": {"used for": [{"head": {"text": "algorithms", "start": 63, "end": 73}, "tail": {"text": "full unification", "start": 37, "end": 53}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also show that : Supporting full unification is costly ; Full unification is not used frequently ; A combination of compile time and run time checks can determine when full unification is not needed .", "output": {"relations": {"conjunction": [{"head": {"text": "compile time", "start": 119, "end": 131}, "tail": {"text": "run time", "start": 136, "end": 144}}], "evaluate for": [{"head": {"text": "compile time", "start": 119, "end": 131}, "tail": {"text": "full unification", "start": 31, "end": 47}}, {"head": {"text": "run time", "start": 136, "end": 144}, "tail": {"text": "full unification", "start": 31, "end": 47}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 2, "end": 8}, "tail": {"text": "error correction", "start": 13, "end": 29}}, {"head": {"text": "ill-formed input", "start": 33, "end": 49}, "tail": {"text": "error correction", "start": 13, "end": 29}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A dialogue acquisition and tracking algorithm is presented along with a description of its implementation in a voice interactive system .", "output": {"relations": {"used for": [{"head": {"text": "dialogue acquisition and tracking algorithm", "start": 2, "end": 45}, "tail": {"text": "voice interactive system", "start": 111, "end": 135}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A series of tests are described that show the power of the error correction methodology when stereotypic dialogue occurs .", "output": {"relations": {"used for": [{"head": {"text": "stereotypic dialogue", "start": 93, "end": 113}, "tail": {"text": "error correction methodology", "start": 59, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Traditional linear Fukunaga-Koontz Transform -LRB- FKT -RRB- -LSB- 1 -RSB- is a powerful discriminative subspaces building approach .", "output": {"relations": {"hyponym of": [{"head": {"text": "linear Fukunaga-Koontz Transform -LRB- FKT -RRB-", "start": 12, "end": 60}, "tail": {"text": "discriminative subspaces building approach", "start": 89, "end": 131}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Previous work has successfully extended FKT to be able to deal with small-sample-size .", "output": {"relations": {"used for": [{"head": {"text": "FKT", "start": 40, "end": 43}, "tail": {"text": "small-sample-size", "start": 68, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional -LRB- kernel -RRB- subspaces and therefore provide enhanced discrimination ability .", "output": {"relations": {"used for": [{"head": {"text": "linear FKT", "start": 38, "end": 48}, "tail": {"text": "it", "start": 30, "end": 32}}, {"head": {"text": "it", "start": 30, "end": 32}, "tail": {"text": "multi-class problem", "start": 73, "end": 92}}, {"head": {"text": "it", "start": 30, "end": 32}, "tail": {"text": "higher dimensional -LRB- kernel -RRB- subspaces", "start": 105, "end": 152}}], "feature of": [{"head": {"text": "it", "start": 30, "end": 32}, "tail": {"text": "discrimination ability", "start": 184, "end": 206}}], "conjunction": [{"head": {"text": "multi-class problem", "start": 73, "end": 92}, "tail": {"text": "higher dimensional -LRB- kernel -RRB- subspaces", "start": 105, "end": 152}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We verify the effectiveness of the proposed Kernel Fukunaga-Koontz Transform by demonstrating its effectiveness in face recognition applications ; however the proposed non-linear generalization can be applied to any other domain specific problems .", "output": {"relations": {"evaluate for": [{"head": {"text": "face recognition applications", "start": 115, "end": 144}, "tail": {"text": "Kernel Fukunaga-Koontz Transform", "start": 44, "end": 76}}], "used for": [{"head": {"text": "non-linear generalization", "start": 168, "end": 193}, "tail": {"text": "domain specific problems", "start": 222, "end": 246}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "While this task has much in common with paraphrases acquisition which aims to discover semantic equivalence between verbs , the main challenge of entailment acquisition is to capture asymmetric , or directional , relations .", "output": {"relations": {"compare": [{"head": {"text": "task", "start": 11, "end": 15}, "tail": {"text": "paraphrases acquisition", "start": 40, "end": 63}}], "used for": [{"head": {"text": "paraphrases acquisition", "start": 40, "end": 63}, "tail": {"text": "semantic equivalence", "start": 87, "end": 107}}, {"head": {"text": "entailment acquisition", "start": 146, "end": 168}, "tail": {"text": "asymmetric , or directional , relations", "start": 183, "end": 222}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Motivated by the intuition that it often underlies the local structure of coherent text , we develop a method that discovers verb entailment using evidence about discourse relations between clauses available in a parsed corpus .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 103, "end": 109}, "tail": {"text": "verb entailment", "start": 125, "end": 140}}, {"head": {"text": "discourse relations", "start": 162, "end": 181}, "tail": {"text": "method", "start": 103, "end": 109}}, {"head": {"text": "parsed corpus", "start": 213, "end": 226}, "tail": {"text": "discourse relations", "start": 162, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In comparison with earlier work , the proposed method covers a much wider range of verb entailment types and learns the mapping between verbs with highly varied argument structures .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 47, "end": 53}, "tail": {"text": "mapping between verbs", "start": 120, "end": 141}}], "feature of": [{"head": {"text": "highly varied argument structures", "start": 147, "end": 180}, "tail": {"text": "mapping between verbs", "start": 120, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrödinger distance transform -LRB- SDT -RRB- representation .", "output": {"relations": {"used for": [{"head": {"text": "shape matching problem", "start": 65, "end": 87}, "tail": {"text": "point cloud matching", "start": 39, "end": 59}}, {"head": {"text": "shape representation", "start": 142, "end": 162}, "tail": {"text": "point clouds", "start": 122, "end": 134}}], "hyponym of": [{"head": {"text": "Schrödinger distance transform -LRB- SDT -RRB- representation", "start": 174, "end": 235}, "tail": {"text": "shape representation", "start": 142, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The SDT representation is an analytic expression and following the theoretical physics literature , can be normalized to have unit L2 norm-making it a square-root density , which is identified with a point on a unit Hilbert sphere , whose intrinsic geometry is fully known .", "output": {"relations": {"hyponym of": [{"head": {"text": "SDT representation", "start": 4, "end": 22}, "tail": {"text": "analytic expression", "start": 29, "end": 48}}], "used for": [{"head": {"text": "square-root density", "start": 151, "end": 170}, "tail": {"text": "it", "start": 88, "end": 90}}], "feature of": [{"head": {"text": "intrinsic geometry", "start": 239, "end": 257}, "tail": {"text": "unit Hilbert sphere", "start": 211, "end": 230}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The Fisher-Rao metric , a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere .", "output": {"relations": {"used for": [{"head": {"text": "natural metric", "start": 26, "end": 40}, "tail": {"text": "space of densities", "start": 49, "end": 67}}, {"head": {"text": "analytic expressions", "start": 77, "end": 97}, "tail": {"text": "geodesic distance", "start": 106, "end": 123}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we use the well known Riemannian framework never before used for point cloud matching , and present a novel matching algorithm .", "output": {"relations": {"used for": [{"head": {"text": "Riemannian framework", "start": 38, "end": 58}, "tail": {"text": "point cloud matching", "start": 81, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques .", "output": {"relations": {"used for": [{"head": {"text": "rigid and non-rigid transformations", "start": 33, "end": 68}, "tail": {"text": "point set matching", "start": 8, "end": 26}}, {"head": {"text": "framework", "start": 77, "end": 86}, "tail": {"text": "point set matching", "start": 8, "end": 26}}, {"head": {"text": "nonlinear optimization techniques", "start": 136, "end": 169}, "tail": {"text": "transformations", "start": 53, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The experiments show that our algorithm outperforms state-of-the-art point set registration algorithms on many quantitative metrics .", "output": {"relations": {"compare": [{"head": {"text": "algorithm", "start": 30, "end": 39}, "tail": {"text": "point set registration algorithms", "start": 69, "end": 102}}], "evaluate for": [{"head": {"text": "quantitative metrics", "start": 111, "end": 131}, "tail": {"text": "algorithm", "start": 30, "end": 39}}, {"head": {"text": "quantitative metrics", "start": 111, "end": 131}, "tail": {"text": "point set registration algorithms", "start": 69, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using natural language processing , we carried out a trend survey on Japanese natural language processing studies that have been done over the last ten years .", "output": {"relations": {"used for": [{"head": {"text": "natural language processing", "start": 6, "end": 33}, "tail": {"text": "trend survey on Japanese natural language processing studies", "start": 53, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper is useful for both recognizing trends in Japanese NLP and constructing a method of supporting trend surveys using NLP .", "output": {"relations": {"used for": [{"head": {"text": "NLP", "start": 61, "end": 64}, "tail": {"text": "trend surveys", "start": 105, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution , intracranial EEG data .", "output": {"relations": {"used for": [{"head": {"text": "high-temporal resolution , intracranial EEG data", "start": 102, "end": 150}, "tail": {"text": "analysis of discrete events", "start": 71, "end": 98}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , previous HFO analysis have assumed a linear manifold , global across time , space -LRB- i.e. recording electrode/channel -RRB- , and individual patients .", "output": {"relations": {"used for": [{"head": {"text": "linear manifold", "start": 47, "end": 62}, "tail": {"text": "HFO analysis", "start": 19, "end": 31}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs -LRB- those occurring during seizures and those occurring due to other processes -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "those", "start": 120, "end": 125}, "tail": {"text": "HFOs", "start": 109, "end": 113}}, {"head": {"text": "those", "start": 120, "end": 125}, "tail": {"text": "HFOs", "start": 109, "end": 113}}], "conjunction": [{"head": {"text": "those", "start": 120, "end": 125}, "tail": {"text": "those", "start": 120, "end": 125}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This analysis provides the foundation for future clinical use of HFO features and guides the analysis for other discrete events , such as individual action potentials or multi-unit activity .", "output": {"relations": {"hyponym of": [{"head": {"text": "action potentials", "start": 149, "end": 166}, "tail": {"text": "discrete events", "start": 112, "end": 127}}, {"head": {"text": "multi-unit activity", "start": 170, "end": 189}, "tail": {"text": "discrete events", "start": 112, "end": 127}}], "conjunction": [{"head": {"text": "action potentials", "start": 149, "end": 166}, "tail": {"text": "multi-unit activity", "start": 170, "end": 189}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper we present ONTOSCORE , a system for scoring sets of concepts on the basis of an ontology .", "output": {"relations": {"used for": [{"head": {"text": "ontology", "start": 94, "end": 102}, "tail": {"text": "system", "start": 39, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We apply our system to the task of scoring alternative speech recognition hypotheses -LRB- SRH -RRB- in terms of their semantic coherence .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 13, "end": 19}, "tail": {"text": "speech recognition hypotheses -LRB- SRH -RRB-", "start": 55, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose an efficient dialogue management for an information navigation system based on a document knowledge base .", "output": {"relations": {"used for": [{"head": {"text": "dialogue management", "start": 24, "end": 43}, "tail": {"text": "information navigation system", "start": 51, "end": 80}}, {"head": {"text": "document knowledge base", "start": 92, "end": 115}, "tail": {"text": "information navigation system", "start": 51, "end": 80}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is expected that incorporation of appropriate N-best candidates of ASR and contextual information will improve the system performance .", "output": {"relations": {"conjunction": [{"head": {"text": "N-best candidates of ASR", "start": 49, "end": 73}, "tail": {"text": "contextual information", "start": 78, "end": 100}}], "used for": [{"head": {"text": "N-best candidates of ASR", "start": 49, "end": 73}, "tail": {"text": "system", "start": 118, "end": 124}}, {"head": {"text": "contextual information", "start": 78, "end": 100}, "tail": {"text": "system", "start": 118, "end": 124}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The system also has several choices in generating responses or confirmations .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 4, "end": 10}, "tail": {"text": "generating responses or confirmations", "start": 39, "end": 76}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , this selection is optimized as minimization of Bayes risk based on reward for correct information presentation and penalty for redundant turns .", "output": {"relations": {"used for": [{"head": {"text": "reward", "start": 83, "end": 89}, "tail": {"text": "minimization of Bayes risk", "start": 47, "end": 73}}, {"head": {"text": "reward", "start": 83, "end": 89}, "tail": {"text": "correct information presentation", "start": 94, "end": 126}}, {"head": {"text": "penalty", "start": 131, "end": 138}, "tail": {"text": "minimization of Bayes risk", "start": 47, "end": 73}}, {"head": {"text": "penalty", "start": 131, "end": 138}, "tail": {"text": "redundant turns", "start": 143, "end": 158}}], "conjunction": [{"head": {"text": "reward", "start": 83, "end": 89}, "tail": {"text": "penalty", "start": 131, "end": 138}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have evaluated this strategy with our spoken dialogue system '' Dialogue Navigator for Kyoto City '' , which also has question-answering capability .", "output": {"relations": {"evaluate for": [{"head": {"text": "spoken dialogue system '' Dialogue Navigator for Kyoto City ''", "start": 41, "end": 103}, "tail": {"text": "strategy", "start": 23, "end": 31}}], "feature of": [{"head": {"text": "question-answering capability", "start": 121, "end": 150}, "tail": {"text": "spoken dialogue system '' Dialogue Navigator for Kyoto City ''", "start": 41, "end": 103}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Effectiveness of the proposed framework was confirmed in the success rate of retrieval and the average number of turns for information access .", "output": {"relations": {"evaluate for": [{"head": {"text": "success rate of retrieval", "start": 61, "end": 86}, "tail": {"text": "framework", "start": 30, "end": 39}}, {"head": {"text": "average number of turns", "start": 95, "end": 118}, "tail": {"text": "framework", "start": 30, "end": 39}}], "conjunction": [{"head": {"text": "success rate of retrieval", "start": 61, "end": 86}, "tail": {"text": "average number of turns", "start": 95, "end": 118}}], "used for": [{"head": {"text": "average number of turns", "start": 95, "end": 118}, "tail": {"text": "information access", "start": 123, "end": 141}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "They are probability , rank , and entropy .", "output": {"relations": {"conjunction": [{"head": {"text": "rank", "start": 23, "end": 27}, "tail": {"text": "entropy", "start": 34, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluated the performance of the three pruning criteria in a real application of Chinese text input in terms of character error rate -LRB- CER -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "pruning criteria", "start": 42, "end": 58}, "tail": {"text": "Chinese text input", "start": 84, "end": 102}}], "evaluate for": [{"head": {"text": "character error rate -LRB- CER -RRB-", "start": 115, "end": 151}, "tail": {"text": "pruning criteria", "start": 42, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also show that the high-performance of rank lies in its strong correlation with error rate .", "output": {"relations": {"evaluate for": [{"head": {"text": "error rate", "start": 83, "end": 93}, "tail": {"text": "rank", "start": 42, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We then present a novel method of combining two criteria in model pruning .", "output": {"relations": {"used for": [{"head": {"text": "method", "start": 24, "end": 30}, "tail": {"text": "model pruning", "start": 60, "end": 73}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes an annotating scheme that encodes honorifics -LRB- respectful words -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "annotating scheme", "start": 23, "end": 40}, "tail": {"text": "honorifics", "start": 54, "end": 64}}], "hyponym of": [{"head": {"text": "honorifics", "start": 54, "end": 64}, "tail": {"text": "respectful words", "start": 71, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Honorifics are used extensively in Japanese , reflecting the social relationship -LRB- e.g. social ranks and age -RRB- of the referents .", "output": {"relations": {"used for": [{"head": {"text": "Honorifics", "start": 0, "end": 10}, "tail": {"text": "Japanese", "start": 35, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This referential information is vital for resolving zero pronouns and improving machine translation outputs .", "output": {"relations": {"used for": [{"head": {"text": "referential information", "start": 5, "end": 28}, "tail": {"text": "zero pronouns", "start": 52, "end": 65}}, {"head": {"text": "referential information", "start": 5, "end": 28}, "tail": {"text": "machine translation outputs", "start": 80, "end": 107}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Visually-guided arm reaching movements are produced by distributed neural networks within parietal and frontal regions of the cerebral cortex .", "output": {"relations": {"used for": [{"head": {"text": "distributed neural networks", "start": 55, "end": 82}, "tail": {"text": "Visually-guided arm reaching movements", "start": 0, "end": 38}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental data indicate that -LRB- I -RRB- single neurons in these regions are broadly tuned to parameters of movement ; -LRB- 2 -RRB- appropriate commands are elaborated by populations of neurons ; -LRB- 3 -RRB- the coordinated action of neu-rons can be visualized using a neuronal population vector -LRB- NPV -RRB- .", "output": {"relations": {"used for": [{"head": {"text": "neuronal population vector -LRB- NPV -RRB-", "start": 277, "end": 319}, "tail": {"text": "coordinated action of neu-rons", "start": 220, "end": 250}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We designed a model of the cortical motor command to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the NPV in motor cortex .", "output": {"relations": {"used for": [{"head": {"text": "model", "start": 14, "end": 19}, "tail": {"text": "cortical motor command", "start": 27, "end": 49}}, {"head": {"text": "NPV", "start": 184, "end": 187}, "tail": {"text": "motor cortex", "start": 191, "end": 203}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model is a two-layer self-organizing neural network which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and -LRB- cartesian -RRB- visual information to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .", "output": {"relations": {"used for": [{"head": {"text": "two-layer self-organizing neural network", "start": 15, "end": 55}, "tail": {"text": "-LRB- angular -RRB- motor commands", "start": 179, "end": 213}}, {"head": {"text": "broadly-tuned -LRB- muscular -RRB- proprioceptive", "start": 71, "end": 120}, "tail": {"text": "two-layer self-organizing neural network", "start": 15, "end": 55}}, {"head": {"text": "-LRB- cartesian -RRB- visual information", "start": 125, "end": 165}, "tail": {"text": "two-layer self-organizing neural network", "start": 15, "end": 55}}], "conjunction": [{"head": {"text": "broadly-tuned -LRB- muscular -RRB- proprioceptive", "start": 71, "end": 120}, "tail": {"text": "-LRB- cartesian -RRB- visual information", "start": 125, "end": 165}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These results suggest the NPV does not give a faithful image of cortical processing during arm reaching movements .", "output": {"relations": {"feature of": [{"head": {"text": "arm reaching movements", "start": 91, "end": 113}, "tail": {"text": "image of cortical processing", "start": 55, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble .", "output": {"relations": {"used for": [{"head": {"text": "base classifiers", "start": 38, "end": 54}, "tail": {"text": "ensemble", "start": 92, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose an alternative way for ensemble construction by resampling pairwise constraints that specify whether a pair of instances belongs to the same class or not .", "output": {"relations": {"used for": [{"head": {"text": "resampling pairwise constraints", "start": 75, "end": 106}, "tail": {"text": "ensemble construction", "start": 50, "end": 71}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using pairwise constraints for ensemble construction is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints .", "output": {"relations": {"used for": [{"head": {"text": "pairwise constraints", "start": 6, "end": 26}, "tail": {"text": "ensemble construction", "start": 31, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "First , we transform the original instances into a new data representation using projections learnt from pairwise constraints .", "output": {"relations": {"used for": [{"head": {"text": "projections", "start": 81, "end": 92}, "tail": {"text": "data representation", "start": 55, "end": 74}}, {"head": {"text": "pairwise constraints", "start": 105, "end": 125}, "tail": {"text": "projections", "start": 81, "end": 92}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Then , we build the base clas-sifiers with the new data representation .", "output": {"relations": {"used for": [{"head": {"text": "data representation", "start": 51, "end": 70}, "tail": {"text": "base clas-sifiers", "start": 20, "end": 37}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose two methods for resampling pairwise constraints following the standard Bagging and Boosting algorithms , respectively .", "output": {"relations": {"used for": [{"head": {"text": "Bagging and Boosting algorithms", "start": 82, "end": 113}, "tail": {"text": "resampling pairwise constraints", "start": 27, "end": 58}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A new algorithm for solving the three dimensional container packing problem is proposed in this paper .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 6, "end": 15}, "tail": {"text": "dimensional container packing problem", "start": 38, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This new algorithm deviates from the traditional approach of wall building and layering .", "output": {"relations": {"compare": [{"head": {"text": "algorithm", "start": 9, "end": 18}, "tail": {"text": "approach of wall building and layering", "start": 49, "end": 87}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We tested our method using all 760 test cases from the OR-Library .", "output": {"relations": {"evaluate for": [{"head": {"text": "OR-Library", "start": 55, "end": 65}, "tail": {"text": "method", "start": 14, "end": 20}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results indicate that the new algorithm is able to achieve an average packing utilization of more than 87 % .", "output": {"relations": {"evaluate for": [{"head": {"text": "average packing utilization", "start": 75, "end": 102}, "tail": {"text": "algorithm", "start": 43, "end": 52}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Current approaches to object category recognition require datasets of training images to be manually prepared , with varying degrees of supervision .", "output": {"relations": {"used for": [{"head": {"text": "approaches", "start": 8, "end": 18}, "tail": {"text": "object category recognition", "start": 22, "end": 49}}, {"head": {"text": "datasets", "start": 58, "end": 66}, "tail": {"text": "approaches", "start": 8, "end": 18}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present an approach that can learn an object category from just its name , by utilizing the raw output of image search engines available on the Internet .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 14, "end": 22}, "tail": {"text": "object category", "start": 41, "end": 56}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We develop a new model , TSI-pLSA , which extends pLSA -LRB- as applied to visual words -RRB- to include spatial information in a translation and scale invariant manner .", "output": {"relations": {"used for": [{"head": {"text": "pLSA", "start": 29, "end": 33}, "tail": {"text": "TSI-pLSA", "start": 25, "end": 33}}, {"head": {"text": "pLSA", "start": 29, "end": 33}, "tail": {"text": "visual words", "start": 75, "end": 87}}], "part of": [{"head": {"text": "spatial information", "start": 105, "end": 124}, "tail": {"text": "TSI-pLSA", "start": 25, "end": 33}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "intra-class variability", "start": 33, "end": 56}}, {"head": {"text": "approach", "start": 4, "end": 12}, "tail": {"text": "unrelated images", "start": 81, "end": 97}}, {"head": {"text": "search engines", "start": 110, "end": 124}, "tail": {"text": "unrelated images", "start": 81, "end": 97}}], "conjunction": [{"head": {"text": "intra-class variability", "start": 33, "end": 56}, "tail": {"text": "unrelated images", "start": 81, "end": 97}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We evaluate the models on standard test sets , showing performance competitive with existing methods trained on hand prepared datasets .", "output": {"relations": {"evaluate for": [{"head": {"text": "test sets", "start": 35, "end": 44}, "tail": {"text": "models", "start": 16, "end": 22}}, {"head": {"text": "test sets", "start": 35, "end": 44}, "tail": {"text": "methods", "start": 93, "end": 100}}], "compare": [{"head": {"text": "methods", "start": 93, "end": 100}, "tail": {"text": "models", "start": 16, "end": 22}}], "used for": [{"head": {"text": "hand prepared datasets", "start": 112, "end": 134}, "tail": {"text": "methods", "start": 93, "end": 100}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of Human-Machine Communication , including Natural Language Processing , Non Verbal and Multimodal Communication .", "output": {"relations": {"conjunction": [{"head": {"text": "speech processing", "start": 82, "end": 99}, "tail": {"text": "Human-Machine Communication", "start": 135, "end": 162}}, {"head": {"text": "Natural Language Processing", "start": 175, "end": 202}, "tail": {"text": "Non Verbal and Multimodal Communication", "start": 205, "end": 244}}], "hyponym of": [{"head": {"text": "Natural Language Processing", "start": 175, "end": 202}, "tail": {"text": "Human-Machine Communication", "start": 135, "end": 162}}, {"head": {"text": "Non Verbal and Multimodal Communication", "start": 205, "end": 244}, "tail": {"text": "Human-Machine Communication", "start": 135, "end": 162}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using lookup table representations .", "output": {"relations": {"used for": [{"head": {"text": "lookup table representations", "start": 231, "end": 259}, "tail": {"text": "analytical expressions", "start": 19, "end": 41}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we describe the pronominal anaphora resolution module of Lucy , a portable English understanding system .", "output": {"relations": {"part of": [{"head": {"text": "pronominal anaphora resolution module", "start": 32, "end": 69}, "tail": {"text": "Lucy", "start": 73, "end": 77}}], "hyponym of": [{"head": {"text": "Lucy", "start": 73, "end": 77}, "tail": {"text": "English understanding system", "start": 91, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB- from general and domain corpora .", "output": {"relations": {"used for": [{"head": {"text": "general and domain corpora", "start": 153, "end": 179}, "tail": {"text": "unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB-", "start": 43, "end": 147}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs .", "output": {"relations": {"used for": [{"head": {"text": "syntactically shallow-parsed corpora", "start": 35, "end": 71}, "tail": {"text": "technique", "start": 13, "end": 22}}, {"head": {"text": "search heuristics", "start": 108, "end": 125}, "tail": {"text": "technique", "start": 13, "end": 22}}], "feature of": [{"head": {"text": "SCFs", "start": 187, "end": 191}, "tail": {"text": "lexico-syntactic knowledge", "start": 154, "end": 180}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Graph-cuts optimization is prevalent in vision and graphics problems .", "output": {"relations": {"used for": [{"head": {"text": "Graph-cuts optimization", "start": 0, "end": 23}, "tail": {"text": "vision and graphics problems", "start": 40, "end": 68}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is thus of great practical importance to parallelize the graph-cuts optimization using to-day 's ubiquitous multi-core machines .", "output": {"relations": {"used for": [{"head": {"text": "multi-core machines", "start": 111, "end": 130}, "tail": {"text": "graph-cuts optimization", "start": 60, "end": 83}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "However , the current best serial algorithm by Boykov and Kolmogorov -LSB- 4 -RSB- -LRB- called the BK algorithm -RRB- still has the superior empirical performance .", "output": {"relations": {"hyponym of": [{"head": {"text": "BK algorithm", "start": 100, "end": 112}, "tail": {"text": "serial algorithm", "start": 27, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we propose a novel adaptive bottom-up approach to parallelize the BK algorithm .", "output": {"relations": {"used for": [{"head": {"text": "adaptive bottom-up approach", "start": 35, "end": 62}, "tail": {"text": "BK algorithm", "start": 82, "end": 94}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Extensive experiments in common applications such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our approach .", "output": {"relations": {"evaluate for": [{"head": {"text": "applications", "start": 32, "end": 44}, "tail": {"text": "approach", "start": 139, "end": 147}}], "hyponym of": [{"head": {"text": "2D/3D image segmentations", "start": 53, "end": 78}, "tail": {"text": "applications", "start": 32, "end": 44}}, {"head": {"text": "3D surface fitting", "start": 83, "end": 101}, "tail": {"text": "applications", "start": 32, "end": 44}}], "conjunction": [{"head": {"text": "2D/3D image segmentations", "start": 53, "end": 78}, "tail": {"text": "3D surface fitting", "start": 83, "end": 101}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the Intersection-over-Union -LRB- IoU -RRB- measure that is used widely in evaluating image segmentation systems .", "output": {"relations": {"hyponym of": [{"head": {"text": "Intersection-over-Union -LRB- IoU -RRB- measure", "start": 128, "end": 175}, "tail": {"text": "evaluation function", "start": 101, "end": 120}}], "evaluate for": [{"head": {"text": "Intersection-over-Union -LRB- IoU -RRB- measure", "start": 128, "end": 175}, "tail": {"text": "image segmentation systems", "start": 210, "end": 236}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Currently , there are two dominant approaches : the first approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions .", "output": {"relations": {"hyponym of": [{"head": {"text": "first", "start": 52, "end": 57}, "tail": {"text": "dominant approaches", "start": 26, "end": 45}}, {"head": {"text": "second approach", "start": 184, "end": 199}, "tail": {"text": "dominant approaches", "start": 26, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our new methods use the EIoEU approximation paired with high quality candidate solutions .", "output": {"relations": {"used for": [{"head": {"text": "EIoEU approximation", "start": 24, "end": 43}, "tail": {"text": "methods", "start": 8, "end": 15}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimentally we show that our new approaches lead to improved performance on both image segmentation tasks .", "output": {"relations": {"evaluate for": [{"head": {"text": "image segmentation tasks", "start": 84, "end": 108}, "tail": {"text": "approaches", "start": 36, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , arc-gv , that can generate a higher margins distribution than AdaBoost and yet performs worse .", "output": {"relations": {"hyponym of": [{"head": {"text": "arc-gv", "start": 103, "end": 109}, "tail": {"text": "boosting algorithm", "start": 82, "end": 100}}], "used for": [{"head": {"text": "arc-gv", "start": 103, "end": 109}, "tail": {"text": "margins distribution", "start": 139, "end": 159}}], "compare": [{"head": {"text": "arc-gv", "start": 103, "end": 109}, "tail": {"text": "AdaBoost", "start": 165, "end": 173}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Although we can reproduce his main finding , we find that the poorer performance of arc-gv can be explained by the increased complexity of the base classifiers it uses , an explanation supported by our experiments and entirely consistent with the margins theory .", "output": {"relations": {"evaluate for": [{"head": {"text": "complexity", "start": 125, "end": 135}, "tail": {"text": "base classifiers", "start": 143, "end": 159}}], "hyponym of": [{"head": {"text": "base classifiers", "start": 143, "end": 159}, "tail": {"text": "arc-gv", "start": 84, "end": 90}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The transfer phase in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than analysis and generation , since it is inherently a conglomeration of individual lexical rules .", "output": {"relations": {"part of": [{"head": {"text": "transfer phase", "start": 4, "end": 18}, "tail": {"text": "machine translation -LRB- MT -RRB- systems", "start": 22, "end": 64}}], "compare": [{"head": {"text": "transfer phase", "start": 4, "end": 18}, "tail": {"text": "analysis", "start": 113, "end": 121}}, {"head": {"text": "transfer phase", "start": 4, "end": 18}, "tail": {"text": "generation", "start": 126, "end": 136}}], "conjunction": [{"head": {"text": "analysis", "start": 113, "end": 121}, "tail": {"text": "generation", "start": 126, "end": 136}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Currently some attempts are being made to use case-based reasoning in machine translation , that is , to make decisions on the basis of translation examples at appropriate pints in MT .", "output": {"relations": {"used for": [{"head": {"text": "case-based reasoning", "start": 46, "end": 66}, "tail": {"text": "machine translation", "start": 70, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper proposes a new type of transfer system , called a Similarity-driven Transfer System -LRB- SimTran -RRB- , for use in such case-based MT -LRB- CBMT -RRB- .", "output": {"relations": {"hyponym of": [{"head": {"text": "Similarity-driven Transfer System -LRB- SimTran -RRB-", "start": 61, "end": 114}, "tail": {"text": "transfer system", "start": 34, "end": 49}}], "used for": [{"head": {"text": "Similarity-driven Transfer System -LRB- SimTran -RRB-", "start": 61, "end": 114}, "tail": {"text": "case-based MT -LRB- CBMT -RRB-", "start": 133, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper addresses the problem of optimal alignment of non-rigid surfaces from multi-view video observations to obtain a temporally consistent representation .", "output": {"relations": {"used for": [{"head": {"text": "optimal alignment of non-rigid surfaces", "start": 36, "end": 75}, "tail": {"text": "temporally consistent representation", "start": 123, "end": 159}}, {"head": {"text": "multi-view video observations", "start": 81, "end": 110}, "tail": {"text": "optimal alignment of non-rigid surfaces", "start": 36, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Conventional non-rigid surface tracking performs frame-to-frame alignment which is subject to the accumulation of errors resulting in a drift over time .", "output": {"relations": {"used for": [{"head": {"text": "frame-to-frame alignment", "start": 49, "end": 73}, "tail": {"text": "non-rigid surface tracking", "start": 13, "end": 39}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Recently , non-sequential tracking approaches have been introduced which reorder the input data based on a dissimilarity measure .", "output": {"relations": {"used for": [{"head": {"text": "dissimilarity measure", "start": 107, "end": 128}, "tail": {"text": "non-sequential tracking approaches", "start": 11, "end": 45}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "They demonstrate a reduced drift and increased robustness to large non-rigid deformations .", "output": {"relations": {"feature of": [{"head": {"text": "robustness", "start": 47, "end": 57}, "tail": {"text": "non-rigid deformations", "start": 67, "end": 89}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Optimisation of the tree for non-sequential tracking , which minimises the errors in temporal consistency due to both the drift and the jumps , is proposed .", "output": {"relations": {"used for": [{"head": {"text": "Optimisation of the tree", "start": 0, "end": 24}, "tail": {"text": "non-sequential tracking", "start": 29, "end": 52}}], "evaluate for": [{"head": {"text": "temporal consistency", "start": 85, "end": 105}, "tail": {"text": "Optimisation of the tree", "start": 0, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A novel cluster tree enforces sequential tracking in local segments of the sequence while allowing global non-sequential traversal among these segments .", "output": {"relations": {"used for": [{"head": {"text": "cluster tree", "start": 8, "end": 20}, "tail": {"text": "sequential tracking in local segments", "start": 30, "end": 67}}, {"head": {"text": "cluster tree", "start": 8, "end": 20}, "tail": {"text": "global non-sequential traversal", "start": 99, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face , cloth and people .", "output": {"relations": {"hyponym of": [{"head": {"text": "face", "start": 79, "end": 83}, "tail": {"text": "non-rigid surfaces", "start": 66, "end": 84}}, {"head": {"text": "cloth", "start": 102, "end": 107}, "tail": {"text": "non-rigid surfaces", "start": 66, "end": 84}}, {"head": {"text": "people", "start": 112, "end": 118}, "tail": {"text": "non-rigid surfaces", "start": 66, "end": 84}}], "conjunction": [{"head": {"text": "face", "start": 79, "end": 83}, "tail": {"text": "cloth", "start": 102, "end": 107}}, {"head": {"text": "cloth", "start": 102, "end": 107}, "tail": {"text": "people", "start": 112, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It demonstrates that the proposed cluster tree achieves better temporal consistency than the previous sequential and non-sequential tracking approaches .", "output": {"relations": {"compare": [{"head": {"text": "cluster tree", "start": 34, "end": 46}, "tail": {"text": "sequential and non-sequential tracking approaches", "start": 102, "end": 151}}], "evaluate for": [{"head": {"text": "temporal consistency", "start": 63, "end": 83}, "tail": {"text": "cluster tree", "start": 34, "end": 46}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Quantitative analysis on a created synthetic facial performance also shows an improvement by the cluster tree .", "output": {"relations": {"evaluate for": [{"head": {"text": "synthetic facial performance", "start": 35, "end": 63}, "tail": {"text": "cluster tree", "start": 97, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The translation of English text into American Sign Language -LRB- ASL -RRB- animation tests the limits of traditional MT architectural designs .", "output": {"relations": {"used for": [{"head": {"text": "MT architectural designs", "start": 118, "end": 142}, "tail": {"text": "translation of English text into American Sign Language -LRB- ASL -RRB- animation", "start": 4, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce spatially complex ASL phenomena called '' classifier predicates . ''", "output": {"relations": {"used for": [{"head": {"text": "semantic representation", "start": 6, "end": 29}, "tail": {"text": "spatially complex ASL phenomena", "start": 106, "end": 137}}, {"head": {"text": "virtual reality 3D scene modeling software", "start": 52, "end": 94}, "tail": {"text": "semantic representation", "start": 6, "end": 29}}], "hyponym of": [{"head": {"text": "classifier predicates", "start": 148, "end": 169}, "tail": {"text": "spatially complex ASL phenomena", "start": 106, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates transfer and direct approaches into a single system .", "output": {"relations": {"conjunction": [{"head": {"text": "transfer", "start": 106, "end": 114}, "tail": {"text": "direct approaches", "start": 119, "end": 136}}], "part of": [{"head": {"text": "transfer", "start": 106, "end": 114}, "tail": {"text": "system", "start": 151, "end": 157}}, {"head": {"text": "direct approaches", "start": 119, "end": 136}, "tail": {"text": "system", "start": 151, "end": 157}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "An extension to the GPSG grammatical formalism is proposed , allowing non-terminals to consist of finite sequences of category labels , and allowing schematic variables to range over such sequences .", "output": {"relations": {"used for": [{"head": {"text": "GPSG grammatical formalism", "start": 20, "end": 46}, "tail": {"text": "extension", "start": 3, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The extension is shown to be sufficient to provide a strongly adequate grammar for crossed serial dependencies , as found in e.g. Dutch subordinate clauses .", "output": {"relations": {"used for": [{"head": {"text": "extension", "start": 4, "end": 13}, "tail": {"text": "grammar", "start": 71, "end": 78}}, {"head": {"text": "grammar", "start": 71, "end": 78}, "tail": {"text": "crossed serial dependencies", "start": 83, "end": 110}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The extension is shown to be parseable by a simple extension to an existing parsing method for GPSG .", "output": {"relations": {"used for": [{"head": {"text": "extension", "start": 4, "end": 13}, "tail": {"text": "extension", "start": 4, "end": 13}}, {"head": {"text": "parsing method", "start": 76, "end": 90}, "tail": {"text": "extension", "start": 4, "end": 13}}, {"head": {"text": "parsing method", "start": 76, "end": 90}, "tail": {"text": "GPSG", "start": 95, "end": 99}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an approach to localizing functional objects in surveillance videos without domain knowledge about semantic object classes that may appear in the scene .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 23, "end": 31}, "tail": {"text": "localizing functional objects", "start": 35, "end": 64}}, {"head": {"text": "surveillance videos", "start": 68, "end": 87}, "tail": {"text": "localizing functional objects", "start": 35, "end": 64}}], "feature of": [{"head": {"text": "semantic object classes", "start": 119, "end": 142}, "tail": {"text": "domain knowledge", "start": 96, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A Bayesian framework is used to probabilistically model : people 's trajectories and intents , constraint map of the scene , and locations of functional objects .", "output": {"relations": {"used for": [{"head": {"text": "Bayesian framework", "start": 2, "end": 20}, "tail": {"text": "people 's trajectories and intents", "start": 58, "end": 92}}, {"head": {"text": "Bayesian framework", "start": 2, "end": 20}, "tail": {"text": "constraint map of the scene", "start": 95, "end": 122}}, {"head": {"text": "Bayesian framework", "start": 2, "end": 20}, "tail": {"text": "locations of functional objects", "start": 129, "end": 160}}], "conjunction": [{"head": {"text": "people 's trajectories and intents", "start": 58, "end": 92}, "tail": {"text": "constraint map of the scene", "start": 95, "end": 122}}, {"head": {"text": "constraint map of the scene", "start": 95, "end": 122}, "tail": {"text": "locations of functional objects", "start": 129, "end": 160}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process is used for inference .", "output": {"relations": {"used for": [{"head": {"text": "data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process", "start": 2, "end": 63}, "tail": {"text": "inference", "start": 76, "end": 85}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our evaluation on videos of public squares and courtyards demonstrates our effectiveness in localizing functional objects and predicting people 's trajectories in unobserved parts of the video footage .", "output": {"relations": {"evaluate for": [{"head": {"text": "videos of public squares and courtyards", "start": 18, "end": 57}, "tail": {"text": "localizing functional objects", "start": 92, "end": 121}}, {"head": {"text": "videos of public squares and courtyards", "start": 18, "end": 57}, "tail": {"text": "predicting people 's trajectories", "start": 126, "end": 159}}], "conjunction": [{"head": {"text": "localizing functional objects", "start": 92, "end": 121}, "tail": {"text": "predicting people 's trajectories", "start": 126, "end": 159}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We propose a process model for hierarchical perceptual sound organization , which recognizes perceptual sounds included in incoming sound signals .", "output": {"relations": {"used for": [{"head": {"text": "process model", "start": 13, "end": 26}, "tail": {"text": "hierarchical perceptual sound organization", "start": 31, "end": 73}}], "part of": [{"head": {"text": "perceptual sounds", "start": 93, "end": 110}, "tail": {"text": "incoming sound signals", "start": 123, "end": 145}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We consider perceptual sound organization as a scene analysis problem in the auditory domain .", "output": {"relations": {"used for": [{"head": {"text": "scene analysis problem", "start": 47, "end": 69}, "tail": {"text": "perceptual sound organization", "start": 12, "end": 41}}], "feature of": [{"head": {"text": "auditory domain", "start": 77, "end": 92}, "tail": {"text": "scene analysis problem", "start": 47, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our model consists of multiple processing modules and a hypothesis network for quantitative integration of multiple sources of information .", "output": {"relations": {"part of": [{"head": {"text": "processing modules", "start": 31, "end": 49}, "tail": {"text": "model", "start": 4, "end": 9}}, {"head": {"text": "hypothesis network", "start": 56, "end": 74}, "tail": {"text": "model", "start": 4, "end": 9}}], "conjunction": [{"head": {"text": "processing modules", "start": 31, "end": 49}, "tail": {"text": "hypothesis network", "start": 56, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "On the hypothesis network , individual information is integrated and an optimal internal model of perceptual sounds is automatically constructed .", "output": {"relations": {"part of": [{"head": {"text": "internal model", "start": 80, "end": 94}, "tail": {"text": "hypothesis network", "start": 7, "end": 25}}], "used for": [{"head": {"text": "internal model", "start": 80, "end": 94}, "tail": {"text": "perceptual sounds", "start": 98, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Based on the model , a music scene analysis system has been developed for acoustic signals of ensemble music , which recognizes rhythm , chords , and source-separated musical notes .", "output": {"relations": {"used for": [{"head": {"text": "music scene analysis system", "start": 23, "end": 50}, "tail": {"text": "acoustic signals of ensemble music", "start": 74, "end": 108}}, {"head": {"text": "music scene analysis system", "start": 23, "end": 50}, "tail": {"text": "rhythm", "start": 128, "end": 134}}, {"head": {"text": "music scene analysis system", "start": 23, "end": 50}, "tail": {"text": "chords", "start": 137, "end": 143}}, {"head": {"text": "music scene analysis system", "start": 23, "end": 50}, "tail": {"text": "source-separated musical notes", "start": 150, "end": 180}}], "conjunction": [{"head": {"text": "rhythm", "start": 128, "end": 134}, "tail": {"text": "chords", "start": 137, "end": 143}}, {"head": {"text": "chords", "start": 137, "end": 143}, "tail": {"text": "source-separated musical notes", "start": 150, "end": 180}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Experimental results show that our method has permitted autonomous , stable and effective information integration to construct the internal model of hierarchical perceptual sounds .", "output": {"relations": {"feature of": [{"head": {"text": "information integration", "start": 90, "end": 113}, "tail": {"text": "method", "start": 35, "end": 41}}], "used for": [{"head": {"text": "information integration", "start": 90, "end": 113}, "tail": {"text": "internal model", "start": 131, "end": 145}}, {"head": {"text": "internal model", "start": 131, "end": 145}, "tail": {"text": "hierarchical perceptual sounds", "start": 149, "end": 179}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We directly investigate a subject of much recent debate : do word sense disambigation models help statistical machine translation quality ?", "output": {"relations": {"used for": [{"head": {"text": "word sense disambigation models", "start": 61, "end": 92}, "tail": {"text": "statistical machine translation quality", "start": 98, "end": 137}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system , we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone .", "output": {"relations": {"used for": [{"head": {"text": "Chinese word sense disambiguation model", "start": 25, "end": 64}, "tail": {"text": "translation candidates", "start": 75, "end": 97}}, {"head": {"text": "translation candidates", "start": 75, "end": 97}, "tail": {"text": "IBM statistical MT system", "start": 112, "end": 137}}], "compare": [{"head": {"text": "word sense disambiguation", "start": 33, "end": 58}, "tail": {"text": "statistical machine translation system", "start": 244, "end": 282}}], "evaluate for": [{"head": {"text": "translation quality", "start": 215, "end": 234}, "tail": {"text": "word sense disambiguation", "start": 33, "end": 58}}, {"head": {"text": "translation quality", "start": 215, "end": 234}, "tail": {"text": "statistical machine translation system", "start": 244, "end": 282}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Image sequence processing techniques are used to study exchange , growth , and transport processes and to tackle key questions in environmental physics and biology .", "output": {"relations": {"used for": [{"head": {"text": "Image sequence processing techniques", "start": 0, "end": 36}, "tail": {"text": "exchange , growth , and transport processes", "start": 55, "end": 98}}], "conjunction": [{"head": {"text": "environmental physics", "start": 130, "end": 151}, "tail": {"text": "biology", "start": 156, "end": 163}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 32, "end": 40}, "tail": {"text": "estimation of the motion field", "start": 49, "end": 79}}], "used for": [{"head": {"text": "estimation of the motion field", "start": 49, "end": 79}, "tail": {"text": "applications", "start": 6, "end": 18}}], "conjunction": [{"head": {"text": "first-order derivatives of the motion field", "start": 170, "end": 213}, "tail": {"text": "dynamical changes of the moving objects", "start": 220, "end": 259}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields -LRB- DVF -RRB- with an accuracy of up to a few hundredth pixels/frame for real-world images .", "output": {"relations": {"used for": [{"head": {"text": "derivative filters", "start": 47, "end": 65}, "tail": {"text": "tensor method", "start": 2, "end": 15}}, {"head": {"text": "real-world images", "start": 194, "end": 211}, "tail": {"text": "pixels/frame", "start": 177, "end": 189}}], "evaluate for": [{"head": {"text": "pixels/frame", "start": 177, "end": 189}, "tail": {"text": "displacement vector fields -LRB- DVF -RRB-", "start": 92, "end": 134}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 4, "end": 12}, "tail": {"text": "tensor method", "start": 20, "end": 33}}, {"head": {"text": "computer-generated sequences", "start": 51, "end": 79}, "tail": {"text": "tensor method", "start": 20, "end": 33}}, {"head": {"text": "calibrated image sequence", "start": 86, "end": 111}, "tail": {"text": "tensor method", "start": 20, "end": 33}}], "conjunction": [{"head": {"text": "computer-generated sequences", "start": 51, "end": 79}, "tail": {"text": "calibrated image sequence", "start": 86, "end": 111}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors , especially the spatial nonuni-formity in the responsivity .", "output": {"relations": {"evaluate for": [{"head": {"text": "accuracy", "start": 25, "end": 33}, "tail": {"text": "motion estimation", "start": 38, "end": 55}}], "used for": [{"head": {"text": "CCD sensors", "start": 102, "end": 113}, "tail": {"text": "motion estimation", "start": 38, "end": 55}}], "feature of": [{"head": {"text": "spatial nonuni-formity", "start": 131, "end": 153}, "tail": {"text": "responsivity", "start": 161, "end": 173}}, {"head": {"text": "responsivity", "start": 161, "end": 173}, "tail": {"text": "CCD sensors", "start": 102, "end": 113}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The application of the techniques to the analysis of plant growth , to ocean surface microturbulence in IR image sequences , and to sediment transport is demonstrated .", "output": {"relations": {"used for": [{"head": {"text": "techniques", "start": 23, "end": 33}, "tail": {"text": "analysis of plant growth", "start": 41, "end": 65}}, {"head": {"text": "techniques", "start": 23, "end": 33}, "tail": {"text": "ocean surface microturbulence in IR image sequences", "start": 71, "end": 122}}, {"head": {"text": "techniques", "start": 23, "end": 33}, "tail": {"text": "sediment transport", "start": 132, "end": 150}}], "conjunction": [{"head": {"text": "analysis of plant growth", "start": 41, "end": 65}, "tail": {"text": "ocean surface microturbulence in IR image sequences", "start": 71, "end": 122}}, {"head": {"text": "ocean surface microturbulence in IR image sequences", "start": 71, "end": 122}, "tail": {"text": "sediment transport", "start": 132, "end": 150}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We present a Czech-English statistical machine translation system which performs tree-to-tree translation of dependency structures .", "output": {"relations": {"used for": [{"head": {"text": "Czech-English statistical machine translation system", "start": 13, "end": 65}, "tail": {"text": "tree-to-tree translation of dependency structures", "start": 81, "end": 130}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The only bilingual resource required is a sentence-aligned parallel corpus .", "output": {"relations": {"used for": [{"head": {"text": "sentence-aligned parallel corpus", "start": 42, "end": 74}, "tail": {"text": "bilingual resource", "start": 9, "end": 27}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We also refer to an evaluation method and plan to compare our system 's output with a benchmark system .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 62, "end": 68}, "tail": {"text": "benchmark system", "start": 86, "end": 102}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper describes the understanding process of the spatial descriptions in Japanese .", "output": {"relations": {"feature of": [{"head": {"text": "Japanese", "start": 78, "end": 86}, "tail": {"text": "spatial descriptions", "start": 54, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "To reconstruct the model , the authors extract the qualitative spatial constraints from the text , and represent them as the numerical constraints on the spatial attributes of the entities .", "output": {"relations": {"used for": [{"head": {"text": "spatial attributes of the entities", "start": 154, "end": 188}, "tail": {"text": "numerical constraints", "start": 125, "end": 146}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such context information is therefore important to characterize the intrinsic representation of a video frame .", "output": {"relations": {"used for": [{"head": {"text": "context information", "start": 5, "end": 24}, "tail": {"text": "intrinsic representation of a video frame", "start": 68, "end": 109}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we present a novel approach to learn the deep video representation by exploring both local and holistic contexts .", "output": {"relations": {"used for": [{"head": {"text": "approach", "start": 35, "end": 43}, "tail": {"text": "deep video representation", "start": 57, "end": 82}}, {"head": {"text": "local and holistic contexts", "start": 101, "end": 128}, "tail": {"text": "approach", "start": 35, "end": 43}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations .", "output": {"relations": {"used for": [{"head": {"text": "triplet sampling mechanism", "start": 28, "end": 54}, "tail": {"text": "local temporal relationship of adjacent frames", "start": 69, "end": 115}}, {"head": {"text": "deep representations", "start": 131, "end": 151}, "tail": {"text": "triplet sampling mechanism", "start": 28, "end": 54}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In addition , we incorporate the graph structure of the video , as a priori , to holistically preserve the inherent correlations among video frames .", "output": {"relations": {"used for": [{"head": {"text": "graph structure of the video", "start": 33, "end": 61}, "tail": {"text": "priori", "start": 69, "end": 75}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Our approach is fully unsupervised and trained in an end-to-end deep convolutional neu-ral network architecture .", "output": {"relations": {"used for": [{"head": {"text": "end-to-end deep convolutional neu-ral network architecture", "start": 53, "end": 111}, "tail": {"text": "approach", "start": 4, "end": 12}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "By extensive experiments , we show that our learned representation can significantly boost several video recognition tasks -LRB- retrieval , classification , and highlight detection -RRB- over traditional video representations .", "output": {"relations": {"compare": [{"head": {"text": "learned representation", "start": 44, "end": 66}, "tail": {"text": "video representations", "start": 205, "end": 226}}], "evaluate for": [{"head": {"text": "video recognition tasks", "start": 99, "end": 122}, "tail": {"text": "learned representation", "start": 44, "end": 66}}, {"head": {"text": "video recognition tasks", "start": 99, "end": 122}, "tail": {"text": "video representations", "start": 205, "end": 226}}], "hyponym of": [{"head": {"text": "retrieval", "start": 129, "end": 138}, "tail": {"text": "video recognition tasks", "start": 99, "end": 122}}, {"head": {"text": "classification", "start": 141, "end": 155}, "tail": {"text": "video recognition tasks", "start": 99, "end": 122}}, {"head": {"text": "highlight detection", "start": 162, "end": 181}, "tail": {"text": "video recognition tasks", "start": 99, "end": 122}}], "conjunction": [{"head": {"text": "retrieval", "start": 129, "end": 138}, "tail": {"text": "classification", "start": 141, "end": 155}}, {"head": {"text": "classification", "start": 141, "end": 155}, "tail": {"text": "highlight detection", "start": 162, "end": 181}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "For mobile speech application , speaker DOA estimation accuracy , interference robustness and compact physical size are three key factors .", "output": {"relations": {"feature of": [{"head": {"text": "speaker DOA estimation accuracy", "start": 32, "end": 63}, "tail": {"text": "mobile speech application", "start": 4, "end": 29}}, {"head": {"text": "interference robustness", "start": 66, "end": 89}, "tail": {"text": "mobile speech application", "start": 4, "end": 29}}, {"head": {"text": "compact physical size", "start": 94, "end": 115}, "tail": {"text": "mobile speech application", "start": 4, "end": 29}}], "conjunction": [{"head": {"text": "speaker DOA estimation accuracy", "start": 32, "end": 63}, "tail": {"text": "interference robustness", "start": 66, "end": 89}}, {"head": {"text": "interference robustness", "start": 66, "end": 89}, "tail": {"text": "compact physical size", "start": 94, "end": 115}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the favorable properties of bispectrum , such as zero value of Gaussian process and different distribution of speech and NSI .", "output": {"relations": {"used for": [{"head": {"text": "It", "start": 0, "end": 2}, "tail": {"text": "favorable properties", "start": 128, "end": 148}}, {"head": {"text": "inter-sensor data ratio model", "start": 31, "end": 60}, "tail": {"text": "AVS", "start": 67, "end": 70}}, {"head": {"text": "bispectrum domain -LRB- BISDR -RRB-", "start": 74, "end": 109}, "tail": {"text": "AVS", "start": 67, "end": 70}}], "hyponym of": [{"head": {"text": "zero value of Gaussian process", "start": 173, "end": 203}, "tail": {"text": "favorable properties", "start": 128, "end": 148}}, {"head": {"text": "distribution of speech and NSI", "start": 218, "end": 248}, "tail": {"text": "favorable properties", "start": 128, "end": 148}}], "conjunction": [{"head": {"text": "zero value of Gaussian process", "start": 173, "end": 203}, "tail": {"text": "distribution of speech and NSI", "start": 218, "end": 248}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Specifically , a reliable bispectrum mask is generated to guarantee that the speaker DOA cues , derived from BISDR , are robust to NSI in terms of speech sparsity and large bispectrum amplitude of the captured signals .", "output": {"relations": {"used for": [{"head": {"text": "bispectrum mask", "start": 26, "end": 41}, "tail": {"text": "speaker DOA cues", "start": 77, "end": 93}}, {"head": {"text": "BISDR", "start": 109, "end": 114}, "tail": {"text": "speaker DOA cues", "start": 77, "end": 93}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Intensive experiments demonstrate an improved performance of our proposed algorithm under various NSI conditions even when SIR is smaller than 0dB .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 74, "end": 83}, "tail": {"text": "NSI conditions", "start": 98, "end": 112}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "In this paper , we want to show how the morphological component of an existing NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB- has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system -LRB- Linguistic String Project - Medical Language Processor -RRB- of the New York University .", "output": {"relations": {"part of": [{"head": {"text": "morphological component", "start": 40, "end": 63}, "tail": {"text": "NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB-", "start": 79, "end": 151}}, {"head": {"text": "language independent modules", "start": 225, "end": 253}, "tail": {"text": "LSP-MLP system -LRB- Linguistic String Project - Medical Language Processor -RRB-", "start": 261, "end": 342}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "The former can take advantage of the language independent developments of the latter , while focusing on idiosyncrasies for Dutch .", "output": {"relations": {"used for": [{"head": {"text": "latter", "start": 78, "end": 84}, "tail": {"text": "former", "start": 4, "end": 10}}, {"head": {"text": "Dutch", "start": 124, "end": 129}, "tail": {"text": "idiosyncrasies", "start": 105, "end": 119}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This general strategy will be illustrated by a practical application , namely the highlighting of relevant information in a patient discharge summary -LRB- PDS -RRB- by means of modern HyperText Mark-Up Language -LRB- HTML -RRB- technology .", "output": {"relations": {"part of": [{"head": {"text": "relevant information", "start": 98, "end": 118}, "tail": {"text": "patient discharge summary -LRB- PDS -RRB-", "start": 124, "end": 165}}], "used for": [{"head": {"text": "HyperText Mark-Up Language -LRB- HTML -RRB- technology", "start": 185, "end": 239}, "tail": {"text": "highlighting of relevant information", "start": 82, "end": 118}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Such an application can be of use for medical administrative purposes in a hospital environment .", "output": {"relations": {"used for": [{"head": {"text": "application", "start": 8, "end": 19}, "tail": {"text": "medical administrative purposes", "start": 38, "end": 69}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements -LRB- e.g. , thesis statements -RRB- .", "output": {"relations": {"part of": [{"head": {"text": "essay-based discourse elements", "start": 112, "end": 142}, "tail": {"text": "CriterionSM Online Essay Evaluation Service", "start": 0, "end": 43}}], "hyponym of": [{"head": {"text": "thesis statements", "start": 156, "end": 173}, "tail": {"text": "essay-based discourse elements", "start": 112, "end": 142}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "We describe a new system that enhances Criterion 's capability , by evaluating multiple aspects of coherence in essays .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 18, "end": 24}, "tail": {"text": "Criterion 's capability", "start": 39, "end": 62}}], "evaluate for": [{"head": {"text": "coherence in essays", "start": 99, "end": 118}, "tail": {"text": "system", "start": 18, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This system identifies features of sentences based on semantic similarity measures and discourse structure .", "output": {"relations": {"used for": [{"head": {"text": "system", "start": 5, "end": 11}, "tail": {"text": "features", "start": 23, "end": 31}}, {"head": {"text": "semantic similarity measures", "start": 54, "end": 82}, "tail": {"text": "features", "start": 23, "end": 31}}, {"head": {"text": "discourse structure", "start": 87, "end": 106}, "tail": {"text": "features", "start": 23, "end": 31}}], "conjunction": [{"head": {"text": "discourse structure", "start": 87, "end": 106}, "tail": {"text": "semantic similarity measures", "start": 54, "end": 82}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements .", "output": {"relations": {"used for": [{"head": {"text": "features", "start": 36, "end": 44}, "tail": {"text": "support vector machine", "start": 2, "end": 24}}, {"head": {"text": "features", "start": 36, "end": 44}, "tail": {"text": "breakdowns in coherence", "start": 56, "end": 79}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Intra-sentential quality is evaluated with rule-based heuristics .", "output": {"relations": {"evaluate for": [{"head": {"text": "rule-based heuristics", "start": 43, "end": 64}, "tail": {"text": "Intra-sentential quality", "start": 0, "end": 24}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "Results indicate that the system yields higher performance than a baseline on all three aspects .", "output": {"relations": {"compare": [{"head": {"text": "system", "start": 26, "end": 32}, "tail": {"text": "baseline", "start": 66, "end": 74}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}
{"input": "This paper presents an algorithm for labeling curvilinear structure at multiple scales in line drawings and edge images Symbolic CURVE-ELEMENT tokens residing in a spatially-indexed and scale-indexed data structure denote circular arcs fit to image data .", "output": {"relations": {"used for": [{"head": {"text": "algorithm", "start": 23, "end": 32}, "tail": {"text": "labeling curvilinear structure", "start": 37, "end": 67}}], "feature of": [{"head": {"text": "line drawings", "start": 90, "end": 103}, "tail": {"text": "labeling curvilinear structure", "start": 37, "end": 67}}, {"head": {"text": "edge images", "start": 108, "end": 119}, "tail": {"text": "labeling curvilinear structure", "start": 37, "end": 67}}], "conjunction": [{"head": {"text": "line drawings", "start": 90, "end": 103}, "tail": {"text": "edge images", "start": 108, "end": 119}}], "part of": [{"head": {"text": "CURVE-ELEMENT tokens", "start": 129, "end": 149}, "tail": {"text": "spatially-indexed and scale-indexed data structure", "start": 164, "end": 214}}]}}, "schema": {"relations": [{"label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B."}, {"label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B."}, {"label": "feature of", "description": "B belongs to A, B is a feature of A, B is under A domain."}, {"label": "hyponym of", "description": "B is a hyponym of A, B is a type of A."}, {"label": "part of", "description": "B is a part of A, A includes B, A contains B."}, {"label": "compare", "description": "Symmetric relation (use blue to denote entity). Opposite of conjunction, compare two models/methods, or listing two opposing entities."}, {"label": "conjunction", "description": "Symmetric relation (use blue to denote entity). Function as similar role or use/incorporate with."}]}}