Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
relation-extraction
File size: 5,206 Bytes
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---
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) |