DocRED: A Large-Scale Document-Level Relation Extraction Dataset

ACL 2019 Yuan YaoDeming YePeng LiXu HanYankai LinZhenghao LiuZhiyuan LiuLixin HuangJie ZhouMaosong Sun

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: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Relation Extraction DocRED Context-Aware F1 50.7 # 12
Relation Extraction DocRED BiLSTM F1 51.06 # 11

Methods used in the Paper


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