HyperAI

Relation Extraction On Ace 2005

Metriken

Cross Sentence
Relation classification F1

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnameCross SentenceRelation classification F1
dual-pointer-network-for-fast-extraction-ofNo80.8
entity-relation-extraction-as-multi-turnNo-
combining-neural-networks-and-log-linearNo67.7
end-to-end-relation-extraction-using-lstms-onNo-
named-entity-recognition-and-relationNo-
asking-effective-and-diverse-questions-aNo-
a-multi-gate-encoder-for-joint-entity-andNo-
hyspa-hybrid-span-generation-for-scalableNo-
autoregressive-structured-prediction-withYes-
a-walk-based-model-on-entity-graphs-forNo64.2
entity-relation-and-event-extraction-withYes-
two-are-better-than-one-joint-entity-andNo-
pack-together-entity-and-relation-extractionYes-
relation-extraction-perspective-fromNo61.3
relation-extraction-among-multiple-entitiesNo80.5
gollie-annotation-guidelines-improve-zero--
extracting-entities-and-relations-with-jointNo-
incremental-joint-extraction-of-entityNo-
span-level-model-for-relation-extractionNo-
a-hierarchical-multi-task-approach-forNo-
end-to-end-neural-relation-extraction-withNo-
a-trigger-sense-memory-flow-framework-forYes-
joint-type-inference-on-entities-andNo-
improved-relation-extraction-with-featureNo58.2
a-partition-filter-network-for-joint-entityNo-
a-general-framework-for-informationYes-
a-frustratingly-easy-approach-for-jointYes-
hyspa-hybrid-span-generation-for-scalable--
iter-iterative-transformer-based-entityYes-
going-out-on-a-limb-joint-extraction-ofNo-