SOTAVerified

Document-level Relation Extraction

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 106 papers

TitleStatusHype
Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability FusionCode0
COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction—0
Enhancing Biomedical Relation Extraction with DirectionalityCode1
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—0
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation ExtractionCode0
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning—0
TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation ExtractionCode0
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction—0
LLM with Relation Classifier for Document-Level Relation ExtractionCode0
GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction—0
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All datasetsBc8DocRED-IEDWIERe-DocRED

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioRex+DirectionalityEvaluation Macro F156.06—Unverified