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الرئيسية
SOTA
التعرف على الكيانات المسماة الصينية
Chinese Named Entity Recognition On Msra
Chinese Named Entity Recognition On Msra
المقاييس
F1
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
F1
Paper Title
Repository
ERNIE 2.0 Base
93.8
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
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NFLAT
94.55
NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
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BERT-MRC
95.75
A Unified MRC Framework for Named Entity Recognition
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FLAT+BERT
96.09
FLAT: Chinese NER Using Flat-Lattice Transformer
-
DiffusionNER
94.91
DiffusionNER: Boundary Diffusion for Named Entity Recognition
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LSTM + Lexicon augment
93.5
Simplify the Usage of Lexicon in Chinese NER
-
FLAT
94.12
FLAT: Chinese NER Using Flat-Lattice Transformer
-
Glyce + BERT
95.54
Glyce: Glyph-vectors for Chinese Character Representations
-
FGN
95.64
FGN: Fusion Glyph Network for Chinese Named Entity Recognition
-
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
-
ZEN (Random Init)
93.24
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
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Baseline + BS
96.26
Boundary Smoothing for Named Entity Recognition
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BERT-CRF (Replicated in AdaSeq)
96.69
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
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TENER
92.74
TENER: Adapting Transformer Encoder for Named Entity Recognition
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Lattice
93.18
Chinese NER Using Lattice LSTM
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W2NER
96.10
Unified Named Entity Recognition as Word-Word Relation Classification
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PIQN
93.48
Parallel Instance Query Network for Named Entity Recognition
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BERT-MRC+DSC
96.72
Dice Loss for Data-imbalanced NLP Tasks
-
CAN-NER Model
92.97
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
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