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 91–100 of 106 papers

TitleStatusHype
Coarse-to-Fine Entity Representations for Document-level Relation ExtractionCode0
Global Context-enhanced Graph Convolutional Networks for Document-level Relation ExtractionCode0
Document-level Relation Extraction with Dual-tier Heterogeneous Graph—0
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction—0
Denoising Relation Extraction from Document-level Distant SupervisionCode1
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction—0
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context PoolingCode1
Double Graph Based Reasoning for Document-level Relation ExtractionCode1
Global-to-Local Neural Networks for Document-Level Relation ExtractionCode1
Entity and Evidence Guided Relation Extraction for DocRED—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