| --- |
| configs: |
| - config_name: ner |
| data_files: ner.parquet |
| default: true |
| - config_name: el |
| data_files: el.parquet |
| - config_name: re |
| data_files: re.parquet |
| |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - found |
| language: |
| - en |
| license: |
| - unknown |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 100<n<1K |
| source_datasets: |
| - original |
| task_categories: |
| - text-classification |
| - token-classification |
| task_ids: |
| - named-entity-recognition |
| - entity-linking-classification |
| - multi-class-classification |
| pretty_name: Text2Tech Curated Documents |
| tags: |
| - structure-prediction |
| - technology |
| - relation extraction |
| - entity linking |
| - named entity recognition |
|
|
| dataset_info: |
| - config_name: ner |
| features: |
| - name: docid |
| dtype: string |
| - name: tokens |
| dtype: string |
| - name: ner_tags |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 917085 |
| num_examples: 135 |
| download_size: 190248 |
| dataset_size: 917085 |
|
|
| - config_name: el |
| features: |
| - name: docid |
| dtype: string |
| - name: tokens |
| dtype: string |
| - name: ner_tags |
| dtype: string |
| - name: entity_mentions |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 1807601 |
| num_examples: 135 |
| download_size: 345738 |
| dataset_size: 1807601 |
|
|
| - config_name: re |
| features: |
| - name: docid |
| dtype: string |
| - name: tokens |
| dtype: string |
| - name: ner_tags |
| dtype: string |
| - name: relations |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 1095051 |
| num_examples: 135 |
| download_size: 210872 |
| dataset_size: 1095051 |
| --- |
| # Dataset Card for Text2Tech Curated Documents |
|
|
| ## Dataset Summary |
|
|
| This dataset is the result of converting a UIMA CAS 0.4 JSON export from the Inception annotation tool into a simplified format suitable for Natural Language Processing tasks. Specifically, it provides configurations for Named Entity Recognition (NER), Entity Linking (EL), and Relation Extraction (RE). |
|
|
| The conversion process utilized the `dkpro-cassis` library to load the original annotations and `spaCy` for tokenization. The final dataset is structured similarly to the DFKI-SLT/mobie dataset to ensure compatibility. |
|
|
| This version of the dataset loader provides configurations for: |
|
|
| * **Named Entity Recognition (ner)**: NER tags use spaCy's BILUO tagging scheme. |
| * **Entity Linking (el)**: Entity mentions are linked to external knowledge bases. |
| * **Relation Extraction (re)**: Relations between entities are annotated. |
|
|
| ## Supported Tasks and Leaderboards |
|
|
| * **Tasks**: Named Entity Recognition, Entity Linking, Relation Extraction |
| * **Leaderboards**: More Information Needed |
|
|
| ## Languages |
|
|
| The text in the dataset is in English. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| #### ner |
|
|
| An example of 'train' looks as follows. |
|
|
| ```json |
| { |
| "docid": "138", |
| "tokens": [ |
| "\"", |
| "Samsung", |
| "takes", |
| "aim", |
| "at", |
| "blood", |
| "pressure", |
| "monitoring", |
| "with", |
| "the", |
| "Galaxy", |
| "Watch", |
| "Active", |
| "..." |
| ], |
| "ner_tags": [ |
| 0, |
| 1, |
| 0, |
| 0, |
| 0, |
| 2, |
| 3, |
| 4, |
| 0, |
| 0, |
| 5, |
| 6, |
| 7, |
| "..." |
| ] |
| } |
| ``` |
|
|
| #### el |
|
|
| An example of 'train' looks as follows. |
|
|
| ```json |
| { |
| "docid": "138", |
| "tokens": [ |
| "\"", |
| "Samsung", |
| "takes", |
| "aim", |
| "at", |
| "blood", |
| "pressure", |
| "monitoring", |
| "with", |
| "the", |
| "Galaxy", |
| "Watch", |
| "Active", |
| "..." |
| ], |
| "ner_tags": [ |
| 0, |
| 1, |
| 0, |
| 0, |
| 0, |
| 2, |
| 3, |
| 4, |
| 0, |
| 0, |
| 5, |
| 6, |
| 7, |
| "..." |
| ], |
| |
| "entity_mentions": [ |
| { |
| "text": "Samsung", |
| "start": 1, |
| "end": 2, |
| "char_start": 1, |
| "char_end": 8, |
| "type": 0, |
| "entity_id": "http://www.wikidata.org/entity/Q124989916" |
| }, |
| "..." |
| ] |
| } |
| ``` |
|
|
| #### re |
|
|
| An example of 'train' looks as follows. |
|
|
| ```json |
| { |
| "docid": "138", |
| "tokens": [ |
| "\"", |
| "Samsung", |
| "takes", |
| "aim", |
| "at", |
| "blood", |
| "pressure", |
| "monitoring", |
| "with", |
| "the", |
| "Galaxy", |
| "Watch", |
| "Active", |
| "..." |
| ], |
| "ner_tags": [ |
| 0, |
| 1, |
| 0, |
| 0, |
| 0, |
| 2, |
| 3, |
| 4, |
| 0, |
| 0, |
| 5, |
| 6, |
| 7, |
| "..." |
| ], |
| "relations": [ |
| { |
| "id": "138-0", |
| "head_start": 706, |
| "head_end": 708, |
| "head_type": 2, |
| "tail_start": 706, |
| "tail_end": 708, |
| "tail_type": 2, |
| "type": 0 |
| }, |
| "..." |
| ] |
| } |
| ``` |
|
|
| ### Data Fields |
|
|
| #### ner |
|
|
| * `docid`: A `string` feature representing the document identifier. |
| * `tokens`: A `list` of `string` features representing the tokens in the document. |
| * `ner_tags`: A `list` of classification labels using spaCy's BILUO tagging scheme. The mapping from ID to tag is as follows: |
|
|
| **BILUO Tagging Scheme:** |
| - **B-** (Begin): First token of a multi-token entity |
| - **I-** (Inside): Inner tokens of a multi-token entity |
| - **L-** (Last): Final token of a multi-token entity |
| - **U-** (Unit): Single token entity |
| - **O** (Outside): Non-entity token |
|
|
| ```json |
| { |
| "O": 0, |
| "U-Organization": 1, |
| "B-Method": 2, |
| "I-Method": 3, |
| "L-Method": 4, |
| "B-Technological System": 5, |
| "I-Technological System": 6, |
| "L-Technological System": 7, |
| "U-Technological System": 8, |
| "U-Method": 9, |
| "B-Material": 10, |
| "L-Material": 11, |
| "I-Material": 12, |
| "B-Organization": 13, |
| "L-Organization": 14, |
| "I-Organization": 15, |
| "U-Material": 16, |
| "B-Technical Field": 17, |
| "L-Technical Field": 18, |
| "I-Technical Field": 19, |
| "U-Technical Field": 20 |
| } |
| ``` |
|
|
| #### el |
|
|
| * `docid`: A `string` feature representing the document identifier. |
| * `tokens`: A `list` of `string` features representing the tokens in the document. |
| * `entity_mentions`: A `list` of `struct` features containing: |
| * `text`: a `string` feature. |
| * `start`: token offset start, a `int32` feature. |
| * `end`: token offset end, a `int32` feature. |
| * `char_start`: character offset start, a `int32` feature. |
| * `char_end`: character offset end, a `int32` feature. |
| * `type`: a classification label. The mapping from ID to entity type is as follows: |
|
|
| ```json |
| { |
| "Organization": 0, |
| "Method": 1, |
| "Technological System": 2, |
| "Material": 3, |
| "Technical Field": 4 |
| } |
| ``` |
|
|
| * `entity_id`: a `string` feature representing the entity identifier from a knowledge base. |
|
|
| #### re |
|
|
| * `docid`: A `string` feature representing the document identifier. |
| * `tokens`: A `list` of `string` features representing the tokens in the document. |
| * `ner_tags`: A `list` of classification labels, corresponding to the NER task. |
| * `relations`: A `list` of `struct` features containing: |
| * `id`: a `string` feature representing the relation identifier. |
| * `head_start`: token offset start of the head entity, an `int32` feature. |
| * `head_end`: token offset end of the head entity, an `int32` feature. |
| * `head_type`: a classification label for the head entity type. |
| * `tail_start`: token offset start of the tail entity, an `int32` feature. |
| * `tail_end`: token offset end of the tail entity, an `int32` feature. |
| * `tail_type`: a classification label for the tail entity type. |
| * `type`: a classification label for the relation type. The mapping from ID to relation type is as follows: |
|
|
| ```json |
| { |
| "ts:executes": 0, |
| "org:develops_or_provides": 1, |
| "ts:contains": 2, |
| "ts:made_of": 3, |
| "ts:uses": 4, |
| "ts:supports": 5, |
| "met:employs": 6, |
| "met:processes": 7, |
| "mat:transformed_to": 8, |
| "org:collaborates": 9, |
| "met:creates": 10, |
| "met:applied_to": 11, |
| "ts:processes": 12 |
| } |
| ``` |
|
|
| ### Data Splits |
|
|
| Please add information about your data splits here. For example: |
|
|
| * **train**: X samples |
| * **validation**: Y samples |
| * **test**: Z samples |
|
|
| ## Dataset Creation |
|
|
| The dataset was created by converting JSON files exported from the Inception annotation tool. The `inception_converter.py` script was used to process these files. This script uses the `dkpro-cassis` library to load the UIMA CAS JSON data and `spaCy` for tokenization and creating BIO tags for the NER task. The data was then split into three separate files for NER, EL, and RE tasks. |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact of Dataset |
|
|
| More Information Needed |
|
|
| ### Discussion of Biases |
|
|
| More Information Needed |
|
|
| ### Other Known Limitations |
|
|
| More Information Needed |
|
|
| ## Additional Information |
|
|
| ### Dataset Curators |
|
|
| Amir Safari |
|
|
| ### Licensing Information |
|
|
| Please specify the license for this dataset. |
|
|
| ### Citation Information |
|
|
| Please provide a BibTeX citation for your dataset. |
|
|
| ```bibtex |
| author = {Amir Safari}, |
| title = {Text2Tech Curated Documents}, |
| year = {2025}, |
| publisher = {Hugging Face} |
| } |
| ``` |
