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Chinese Named Entity Recognition On Ontonotes

Metrics

F1
Precision
Recall

Results

Performance results of various models on this benchmark

Paper TitleRepository
BERT-MRC+DSC84.47--Dice Loss for Data-imbalanced NLP Tasks
W2NER83.08--Unified Named Entity Recognition as Word-Word Relation Classification
Baseline + BS82.83--Boundary Smoothing for Named Entity Recognition
BERT-MRC82.11--A Unified MRC Framework for Named Entity Recognition
FGN82.04--FGN: Fusion Glyph Network for Chinese Named Entity Recognition
FLAT+BERT81.82--FLAT: Chinese NER Using Flat-Lattice Transformer
AESINER81.18--Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Glyce + BERT80.6281.8781.4Glyce: Glyph-vectors for Chinese Character Representations
SLK-NER80.2--SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
NFLAT77.2175.1779.37NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
FLAT76.45--FLAT: Chinese NER Using Flat-Lattice Transformer
LSTM + Lexicon augment75.54--Simplify the Usage of Lexicon in Chinese NER
LGN74.8976.1373.68A Lexicon-Based Graph Neural Network for Chinese NER-
Lattice73.88--Chinese NER Using Lattice LSTM
CAN-NER Model73.6475.0572.29CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
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Chinese Named Entity Recognition On Ontonotes | SOTA | HyperAI