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Chinese Named Entity Recognition On Ontonotes

Métriques

F1
Precision
Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
F1
Precision
Recall
Paper TitleRepository
NFLAT77.2175.1779.37NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition-
Lattice73.88--Chinese NER Using Lattice LSTM-
LGN74.8976.1373.68A Lexicon-Based Graph Neural Network for Chinese NER-
FLAT76.45--FLAT: Chinese NER Using Flat-Lattice Transformer-
SLK-NER80.2--SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER-
LSTM + Lexicon augment75.54--Simplify the Usage of Lexicon in Chinese NER-
W2NER83.08--Unified Named Entity Recognition as Word-Word Relation Classification-
Baseline + BS82.83--Boundary Smoothing for Named Entity Recognition-
AESINER81.18--Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information-
CAN-NER Model73.6475.0572.29CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition-
BERT-MRC82.11--A Unified MRC Framework for Named Entity Recognition-
FLAT+BERT81.82--FLAT: Chinese NER Using Flat-Lattice Transformer-
FGN82.04--FGN: Fusion Glyph Network for Chinese Named Entity Recognition-
BERT-MRC+DSC84.47--Dice Loss for Data-imbalanced NLP Tasks-
Glyce + BERT80.6281.8781.4Glyce: Glyph-vectors for Chinese Character Representations-
0 of 15 row(s) selected.