Chinese Named Entity Recognition On Resume

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	F1	Paper Title	Repository
Baseline + BS	96.66	Boundary Smoothing for Named Entity Recognition	-
Lattice	94.46	Chinese NER Using Lattice LSTM	-
AESINER	96.62	Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information	-
FLAT	95.45	FLAT: Chinese NER Using Flat-Lattice Transformer	-
Glyce + BERT	96.54	Glyce: Glyph-vectors for Chinese Character Representations	-
FLAT+BERT	95.86	FLAT: Chinese NER Using Flat-Lattice Transformer	-
FGN	96.79	FGN: Fusion Glyph Network for Chinese Named Entity Recognition	-
TENER	95	TENER: Adapting Transformer Encoder for Named Entity Recognition	-
LSTM + Lexicon augment	95.59	Simplify the Usage of Lexicon in Chinese NER	-
SLK-NER	95.8	SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER	-
BERT-CRF (Replicated in AdaSeq)	96.87	Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning	-
NFLAT	95.58	NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition	-
CAN-NER Model	94.94	CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition	-

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