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Relation Extraction On Conll04

Metrics

RE+ Micro F1

Results

Performance results of various models on this benchmark

Model Name
RE+ Micro F1
Paper TitleRepository
ReLiK-Large78.1ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
multi-head + AT-Adversarial training for multi-context joint entity and relation extraction
REBEL75.4REBEL: Relation Extraction By End-to-end Language generation
Global67.8End-to-End Neural Relation Extraction with Global Optimization-
TriMF72.35A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Relation-Metric with AT-Neural Metric Learning for Fast End-to-End Relation Extraction-
Biaffine attention-End-to-end neural relation extraction using deep biaffine attention
Table Representation61--
SpERT71.47Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Multi-turn QA68.9Entity-Relation Extraction as Multi-Turn Question Answering
ASP+T0-3B76.3Autoregressive Structured Prediction with Language Models
TANL72.6Structured Prediction as Translation between Augmented Natural Languages
Deeper71.08Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Table-Sequence73.6Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
TablERT72.6Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations
multi-head-Joint entity recognition and relation extraction as a multi-head selection problem
0 of 16 row(s) selected.
Relation Extraction On Conll04 | SOTA | HyperAI