SOTAVerified

Document-level Relation Extraction

Document-level RE aim to identify the relations of various entity pairs expressed across multiple sentences.

Papers

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Showing 61–70 of 106 papers

TitleStatusHype
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and GenesCode1
A sequence-to-sequence approach for document-level relation extractionCode1
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction—0
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction—0
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge DistillationCode1
CorefDRE: Document-level Relation Extraction with coreference resolution—0
Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning—0
An Empirical Study on Relation Extraction in the Biomedical Domain—0
Translating Embeddings in Document for Modeling Multi-relational Graphs—0
Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning—0
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All datasetsBc8DocRED-IEDWIERe-DocRED

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioRex+DirectionalityEvaluation Macro F156.06—Unverified
#ModelMetricClaimedVerifiedStatus
1REXELRelation F160.1—Unverified
#ModelMetricClaimedVerifiedStatus
1VaeDiff-DocREF10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VaeDiff-DocREF10.79—Unverified