Relation Extraction On Ace 2005

Métriques

Cross Sentence

Relation classification F1

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Cross Sentence	Relation classification F1	Paper Title	Repository
Dual Pointer Network(multi-head)	No	80.8	Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence	-
Multi-turn QA	No	-	Entity-Relation Extraction as Multi-Turn Question Answering
RNN+CNN	No	67.7	Combining Neural Networks and Log-linear Models to Improve Relation Extraction	-
SPTree	No	-	End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
TablERT	No	-	Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations
MRC4ERE++	No	-	Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction
MGE	No	-	A Multi-Gate Encoder for Joint Entity and Relation Extraction	-
HySPA	No	-	HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
ASP+T5-3B	Yes	-	Autoregressive Structured Prediction with Language Models
Walk-based model	No	64.2	A Walk-based Model on Entity Graphs for Relation Extraction
DYGIE++	Yes	-	Entity, Relation, and Event Extraction with Contextualized Span Representations
Table-Sequence	No	-	Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
PL-Marker	Yes	-	Packed Levitated Marker for Entity and Relation Extraction
CNN	No	61.3	-	-
Dual Pointer Network	No	80.5	Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism	-
GoLLIE	-	-	GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
MRT	No	-	Extracting Entities and Relations with Joint Minimum Risk Training	-
Joint w/ Global	No	-	-	-
Span-level	No	-	Span-Level Model for Relation Extraction	-
Hierarchical Multi-task	No	-	A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

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