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Relation Extraction On Semeval 2010 Task 8

Metrics

F1

Results

Performance results of various models on this benchmark

Paper Title
SP91.9Relation Classification as Two-way Span-Prediction
RIFRE91.3Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
REDN91Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
SPOT90.6SPOT: Knowledge-Enhanced Language Representations for Information Extraction
KLG90.5Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
RELA90.4Sequence Generation with Label Augmentation for Relation Extraction
Skeleton-Aware BERT90.36Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
KnowPrompt90.3KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
LUKE90.3SPOT: Knowledge-Enhanced Language Representations for Information Extraction
EPGNN90.2Improving Relation Classification by Entity Pair Graph
RE-DMP + XLNet89.90Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking
A-GCN + BERT89.85Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
BERTEM+MTB89.5Matching the Blanks: Distributional Similarity for Relation Learning
BERT89.4SPOT: Knowledge-Enhanced Language Representations for Information Extraction
R-BERT89.25Enriching Pre-trained Language Model with Entity Information for Relation Classification
CorefBERT89.2SPOT: Knowledge-Enhanced Language Representations for Information Extraction
KnowBert-W+W89.1Knowledge Enhanced Contextual Word Representations
KnowBERT89.1SPOT: Knowledge-Enhanced Language Representations for Information Extraction
Entity-Aware BERT89.0Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
SpanBERT88.8SPOT: Knowledge-Enhanced Language Representations for Information Extraction
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Relation Extraction On Semeval 2010 Task 8 | SOTA | HyperAI