Relation Extraction On Semeval 2010 Task 8

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		Paper Title
SP	91.9	Relation Classification as Two-way Span-Prediction
RIFRE	91.3	Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
REDN	91	Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
SPOT	90.6	SPOT: Knowledge-Enhanced Language Representations for Information Extraction
KLG	90.5	Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
RELA	90.4	Sequence Generation with Label Augmentation for Relation Extraction
Skeleton-Aware BERT	90.36	Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
KnowPrompt	90.3	KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
LUKE	90.3	SPOT: Knowledge-Enhanced Language Representations for Information Extraction
EPGNN	90.2	Improving Relation Classification by Entity Pair Graph
RE-DMP + XLNet	89.90	Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking
A-GCN + BERT	89.85	Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
BERTEM+MTB	89.5	Matching the Blanks: Distributional Similarity for Relation Learning
BERT	89.4	SPOT: Knowledge-Enhanced Language Representations for Information Extraction
R-BERT	89.25	Enriching Pre-trained Language Model with Entity Information for Relation Classification
CorefBERT	89.2	SPOT: Knowledge-Enhanced Language Representations for Information Extraction
KnowBert-W+W	89.1	Knowledge Enhanced Contextual Word Representations
KnowBERT	89.1	SPOT: Knowledge-Enhanced Language Representations for Information Extraction
Entity-Aware BERT	89.0	Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
SpanBERT	88.8	SPOT: Knowledge-Enhanced Language Representations for Information Extraction

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Relation Extraction On Semeval 2010 Task 8

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