README.md

---
dataset_info:
  features:
  - name: entities
    list:
    - name: end
      dtype: int64
    - name: start
      dtype: int64
    - name: type
      dtype: string
  - name: tokens
    sequence: string
  - name: relations
    list:
    - name: head
      dtype: int64
    - name: tail
      dtype: int64
    - name: type
      dtype: string
  - name: orig_id
    dtype: int64
  splits:
  - name: train
    num_bytes: 358752
    num_examples: 922
  - name: validation
    num_bytes: 94688
    num_examples: 231
  - name: test
    num_bytes: 114248
    num_examples: 288
  download_size: 204955
  dataset_size: 567688
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- token-classification
language:
- en
tags:
- relation-extraction
pretty_name: CoNLL04
size_categories:
- 1K<n<10K
---
# Dataset Card for CoNLL04

## Dataset Description

- **Repository:** https://github.com/lavis-nlp/spert
- **Paper:** https://aclanthology.org/W04-2401/
- **Benchmark:** https://paperswithcode.com/sota/relation-extraction-on-conll04

#### Dataset Summary

<!-- Provide a quick summary of the dataset. -->

The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.
The data in this repository was converted from ConLL04 format to JSONL format in https://github.com/lavis-nlp/spert/blob/master/scripts/conversion/convert_conll04.py

The original data can be found here: https://cogcomp.seas.upenn.edu/page/resource_view/43

The sentences in this dataset are tokenized and are annotated with entities (`Peop`, `Loc`, `Org`, `Other`) and relations (`Located_In`, `Work_For`, `OrgBased_In`, `Live_In`, `Kill`).

### Languages

The language in the dataset is English.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

### Dataset Instances

An example of 'train' looks as follows:
```json
{
  "tokens": ["Newspaper", "`", "Explains", "'", "U.S.", "Interests", "Section", "Events", "FL1402001894", "Havana", "Radio", "Reloj", "Network", "in", "Spanish", "2100", "GMT", "13", "Feb", "94"],
  "entities": [
    {"type": "Loc", "start": 4, "end": 5},
    {"type": "Loc", "start": 9, "end": 10},
    {"type": "Org", "start": 10, "end": 13},
    {"type": "Other", "start": 15, "end": 17},
    {"type": "Other", "start": 17, "end": 20}
  ],
  "relations": [
    {"type": "OrgBased_In", "head": 2, "tail": 1}
  ],
  "orig_id": 3255
}
```

### Data Fields

- `tokens`: the text of this example, a `string` feature.
- `entities`: list of entities
    - `type`: entity type, a `string` feature.
    - `start`: start token index of entity, a `int32` feature.
    - `end`: exclusive end token index of entity, a `int32` feature.
- `relations`: list of relations
    - `type`: relation type, a `string` feature.
    - `head`: index of head entity, a `int32` feature.
    - `tail`: index of tail entity, a `int32` feature.


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@inproceedings{roth-yih-2004-linear,
    title = "A Linear Programming Formulation for Global Inference in Natural Language Tasks",
    author = "Roth, Dan  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the Eighth Conference on Computational Natural Language Learning ({C}o{NLL}-2004) at {HLT}-{NAACL} 2004",
    month = may # " 6 - " # may # " 7",
    year = "2004",
    address = "Boston, Massachusetts, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W04-2401",
    pages = "1--8",
}
@article{eberts-ulges2019spert,
  author       = {Markus Eberts and
                  Adrian Ulges},
  title        = {Span-based Joint Entity and Relation Extraction with Transformer Pre-training},
  journal      = {CoRR},
  volume       = {abs/1909.07755},
  year         = {2019},
  url          = {http://arxiv.org/abs/1909.07755},
  eprinttype    = {arXiv},
  eprint       = {1909.07755},
  timestamp    = {Mon, 23 Sep 2019 18:07:15 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1909-07755.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

**APA:**

- Roth, D., & Yih, W. (2004). A linear programming formulation for global inference in natural language tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004 (pp. 1-8). Boston, Massachusetts, USA: Association for Computational Linguistics. https://aclanthology.org/W04-2401
- Eberts, M., & Ulges, A. (2019). Span-based joint entity and relation extraction with transformer pre-training. CoRR, abs/1909.07755. http://arxiv.org/abs/1909.07755

## Dataset Card Authors

[@phucdev](https://github.com/phucdev)