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Relation Extraction On Cdr

Metrics

F1

Results

Performance results of various models on this benchmark

Model Name
F1
Paper TitleRepository
SAISORE+CR+ET-SciBERT79SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
LSR w/o MDP Nodes64.8Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
SSANBiaffine68.7Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
CGM2IR-SciBERTbase73.8Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning-
SciBERT-ATLOPBASE69.4Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
DocuNet-SciBERTbase76.3Document-level Relation Extraction as Semantic Segmentation
seq2rel (entity hinting)67.2A sequence-to-sequence approach for document-level relation extraction
Dense-CCNet-SciBERTbase77.06A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction-
BioRelation-Extraction_BERT_SDP_Triplets65Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information
DRE-MIR-SciBERT76.6A Masked Image Reconstruction Network for Document-level Relation Extraction-
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Relation Extraction On Cdr | SOTA | HyperAI