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