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Chinese Named Entity Recognition On Resume

Metrics

F1

Results

Performance results of various models on this benchmark

Model Name
F1
Paper TitleRepository
Baseline + BS96.66Boundary Smoothing for Named Entity Recognition-
Lattice94.46Chinese NER Using Lattice LSTM-
AESINER96.62Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information-
FLAT95.45FLAT: Chinese NER Using Flat-Lattice Transformer-
Glyce + BERT96.54Glyce: Glyph-vectors for Chinese Character Representations-
FLAT+BERT95.86FLAT: Chinese NER Using Flat-Lattice Transformer-
FGN96.79FGN: Fusion Glyph Network for Chinese Named Entity Recognition-
TENER95TENER: Adapting Transformer Encoder for Named Entity Recognition-
LSTM + Lexicon augment95.59Simplify the Usage of Lexicon in Chinese NER-
SLK-NER95.8SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER-
BERT-CRF (Replicated in AdaSeq)96.87Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning-
NFLAT95.58NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition-
CAN-NER Model94.94CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition-
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Chinese Named Entity Recognition On Resume | SOTA | HyperAI