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---
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}
}
``` |