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Dataset: docred 🏷
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#### Description

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.

#### Citation

@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}
}

#### Models trained or fine-tuned on docred

None yet. Start fine-tuning now =)