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SOTA
中国語固有表現認識
Chinese Named Entity Recognition On Msra
Chinese Named Entity Recognition On Msra
評価指標
F1
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
F1
Paper Title
BERT-MRC+DSC
96.72
Dice Loss for Data-imbalanced NLP Tasks
BERT-CRF (Replicated in AdaSeq)
96.69
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Baseline + BS
96.26
Boundary Smoothing for Named Entity Recognition
W2NER
96.10
Unified Named Entity Recognition as Word-Word Relation Classification
FLAT+BERT
96.09
FLAT: Chinese NER Using Flat-Lattice Transformer
BERT-MRC
95.75
A Unified MRC Framework for Named Entity Recognition
FGN
95.64
FGN: Fusion Glyph Network for Chinese Named Entity Recognition
Glyce + BERT
95.54
Glyce: Glyph-vectors for Chinese Character Representations
ZEN (Init with Chinese BERT)
95.25
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
ERNIE 2.0 Large
95
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
DiffusionNER
94.91
DiffusionNER: Boundary Diffusion for Named Entity Recognition
NFLAT
94.55
NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
FLAT
94.12
FLAT: Chinese NER Using Flat-Lattice Transformer
ERNIE 2.0 Base
93.8
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
ERNIE
93.8
ERNIE: Enhanced Representation through Knowledge Integration
LSTM + Lexicon augment
93.5
Simplify the Usage of Lexicon in Chinese NER
PIQN
93.48
Parallel Instance Query Network for Named Entity Recognition
ZEN (Random Init)
93.24
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
Lattice
93.18
Chinese NER Using Lattice LSTM
CAN-NER Model
92.97
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
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Chinese Named Entity Recognition On Msra | SOTA | HyperAI超神経