Datasets:
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
paperswithcode_id: docred
pretty_name: DocRED
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
Dataset Card for DocRED
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: https://github.com/thunlp/DocRED
- Paper: DocRED: A Large-Scale Document-Level Relation Extraction Dataset
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 20.03 MB
- Size of the generated dataset: 19.19 MB
- Total amount of disk used: 39.23 MB
Dataset Summary
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text. - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
default
- Size of downloaded dataset files: 20.03 MB
- Size of the generated dataset: 19.19 MB
- Total amount of disk used: 39.23 MB
An example of 'train_annotated' looks as follows.
{
"labels": {
"evidence": [[0]],
"head": [0],
"relation_id": ["P1"],
"relation_text": ["is_a"],
"tail": [0]
},
"sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]],
"title": "Title of the document",
"vertexSet": [[{
"name": "sentence",
"pos": [3],
"sent_id": 0,
"type": "NN"
}, {
"name": "sentence",
"pos": [3],
"sent_id": 1,
"type": "NN"
}], [{
"name": "This",
"pos": [0],
"sent_id": 0,
"type": "NN"
}]]
}
Data Fields
The data fields are the same among all splits.
default
title: astringfeature.sents: a dictionary feature containing:feature: astringfeature.
name: astringfeature.sent_id: aint32feature.pos: alistofint32features.type: astringfeature.labels: a dictionary feature containing:head: aint32feature.tail: aint32feature.relation_id: astringfeature.relation_text: astringfeature.evidence: alistofint32features.
Data Splits
| name | train_annotated | train_distant | validation | test |
|---|---|---|---|---|
| default | 3053 | 101873 | 998 | 1000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{yao2019DocRED,
title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},
author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},
booktitle={Proceedings of ACL 2019},
year={2019}
}
Contributions
Thanks to @ghomasHudson, @thomwolf, @lhoestq for adding this dataset.