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Relation Extraction On Conll04

Metriken

RE+ Micro F1

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	RE+ Micro F1	Paper Title	Repository
ReLiK-Large	78.1	ReLiK: 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
REBEL	75.4	REBEL: Relation Extraction By End-to-end Language generation
Global	67.8	End-to-End Neural Relation Extraction with Global Optimization	-
TriMF	72.35	A 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 Representation	61	-	-
SpERT	71.47	Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Multi-turn QA	68.9	Entity-Relation Extraction as Multi-Turn Question Answering
ASP+T0-3B	76.3	Autoregressive Structured Prediction with Language Models
TANL	72.6	Structured Prediction as Translation between Augmented Natural Languages
Deeper	71.08	Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Table-Sequence	73.6	Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
TablERT	72.6	Named 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

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