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 81–90 of 106 papers

TitleStatusHype
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling—0
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction—0
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning—0
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming—0
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey—0
CorefDRE: Document-level Relation Extraction with coreference resolution—0
ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction—0
Rethinking Document-Level Relation Extraction: A Reality Check—0
A Comprehensive Survey of Document-level Relation Extraction (2016-2023)—0
COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction—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