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Datasets/DocRED

DocRED

TextsUnknownIntroduced 2019-01-01

DocRED (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. 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, the dataset provides large-scale distantly supervised data.

DocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents.

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

Benchmarks

Few-Shot Learning/F1 (1-Doc)Few-Shot Learning/F1 (3-Doc)Information Extraction/Relation F1Meta-Learning/F1 (1-Doc)Meta-Learning/F1 (3-Doc)Relation Classification/F1 (1-Doc)Relation Classification/F1 (3-Doc)Relation Extraction/F1Relation Extraction/Ign F1Relation Extraction/F1 (1-Doc)Relation Extraction/F1 (3-Doc)Relation Extraction/Relation F1

Related Benchmarks

DocRED-IE/Coreference Resolution/Avg F1DocRED-IE/Document-level Relation Extraction/Relation F1DocRED-IE/Entity Disambiguation/Avg F1DocRED-IE/Entity Typing/Avg F1DocRED-IE/Information Extraction/Relation F1DocRED-IE/Relation Extraction/Relation F1

Statistics

Papers
155
Benchmarks
12

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Tasks

Document-level Closed Information ExtractionFew-Shot LearningFew-Shot Relation ClassificationInformation ExtractionJoint Entity and Relation ExtractionMeta-LearningRelation ClassificationRelation Extraction