| # 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 and ease of use with the Hugging Face ecosystem. | |
| 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} | |
| } | |
| ``` |