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Relation Extraction On Ace 2004

Metriken

Cross Sentence

NER Micro F1

RE+ Micro F1

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Cross Sentence	NER Micro F1	RE+ Micro F1	Paper Title	Repository
Multi-turn QA	No	83.6	49.4	Entity-Relation Extraction as Multi-Turn Question Answering
PFN	No	89.3	62.5	A Partition Filter Network for Joint Entity and Relation Extraction
Ours: cross-sentence ALB	Yes	90.3	62.2	A Frustratingly Easy Approach for Entity and Relation Extraction
PL-Marker	Yes	90.4	66.5	Packed Levitated Marker for Entity and Relation Extraction
Joint w/ Global	No	79.7	45.3	-	-
SPTree	No	81.8	48.4	End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Attention	No	79.6	45.7	Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees	-
Table-Sequence	No	88.6	59.6	Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
multi-head + AT	No	81.64	47.45	Adversarial training for multi-context joint entity and relation extraction
multi-head	No	81.16	47.14	Joint entity recognition and relation extraction as a multi-head selection problem
DyGIE	Yes	87.4	-	A General Framework for Information Extraction using Dynamic Span Graphs

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Relation Extraction On Ace 2004 | SOTA | HyperAI