Dataset and code for baselines for DocRED: A Large-Scale Document-Level Relation Extraction Dataset
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.
If you are interested in our dataset, you are welcome to join in the Codalab competition at DocRED
Sorry, we have changed the computing method for Ignore F1. The numbers in origin paper and Codalab link have been updated.
If you use the dataset or the code, please cite this paper:
@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}
}