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HyperAI超神経

Relation Extraction On Ace 2005

評価指標

Cross Sentence
Relation classification F1

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Cross Sentence
Relation classification F1
Paper TitleRepository
Dual Pointer Network(multi-head)No80.8Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence-
Multi-turn QANo-Entity-Relation Extraction as Multi-Turn Question Answering
RNN+CNNNo67.7Combining Neural Networks and Log-linear Models to Improve Relation Extraction-
SPTreeNo-End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
TablERTNo-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
MGENo-A Multi-Gate Encoder for Joint Entity and Relation Extraction-
HySPANo-HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
ASP+T5-3BYes-Autoregressive Structured Prediction with Language Models
Walk-based modelNo64.2A Walk-based Model on Entity Graphs for Relation Extraction
DYGIE++Yes-Entity, Relation, and Event Extraction with Contextualized Span Representations
Table-SequenceNo-Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
PL-MarkerYes-Packed Levitated Marker for Entity and Relation Extraction
CNNNo61.3--
Dual Pointer NetworkNo80.5Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism-
GoLLIE--GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
MRTNo-Extracting Entities and Relations with Joint Minimum Risk Training-
Joint w/ GlobalNo---
Span-levelNo-Span-Level Model for Relation Extraction-
Hierarchical Multi-taskNo-A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
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