configs:
- config_name: ner
data_files: ner.parquet
default: true
- config_name: el
data_files: el.parquet
- config_name: re
data_files: re.parquet
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.
{
"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.
{
"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.
{
"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: Astringfeature representing the document identifier.tokens: Alistofstringfeatures representing the tokens in the document.ner_tags: Alistof 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
{
"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: Astringfeature representing the document identifier.tokens: Alistofstringfeatures representing the tokens in the document.entity_mentions: Alistofstructfeatures containing:text: astringfeature.start: token offset start, aint32feature.end: token offset end, aint32feature.char_start: character offset start, aint32feature.char_end: character offset end, aint32feature.type: a classification label. The mapping from ID to entity type is as follows:
{
"Organization": 0,
"Method": 1,
"Technological System": 2,
"Material": 3,
"Technical Field": 4
}
entity_id: astringfeature representing the entity identifier from a knowledge base.
re
docid: Astringfeature representing the document identifier.tokens: Alistofstringfeatures representing the tokens in the document.ner_tags: Alistof classification labels, corresponding to the NER task.relations: Alistofstructfeatures containing:id: astringfeature representing the relation identifier.head_start: token offset start of the head entity, anint32feature.head_end: token offset end of the head entity, anint32feature.head_type: a classification label for the head entity type.tail_start: token offset start of the tail entity, anint32feature.tail_end: token offset end of the tail entity, anint32feature.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:
{
"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.
author = {Amir Safari},
title = {Text2Tech Curated Documents},
year = {2025},
publisher = {Hugging Face}
}