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SOTA
Chinese Named Entity Recognition
Chinese Named Entity Recognition On Ontonotes
Chinese Named Entity Recognition On Ontonotes
Metriken
F1
Precision
Recall
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
F1
Precision
Recall
Paper Title
Repository
NFLAT
77.21
75.17
79.37
NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
Lattice
73.88
-
-
Chinese NER Using Lattice LSTM
LGN
74.89
76.13
73.68
A Lexicon-Based Graph Neural Network for Chinese NER
-
FLAT
76.45
-
-
FLAT: Chinese NER Using Flat-Lattice Transformer
SLK-NER
80.2
-
-
SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
LSTM + Lexicon augment
75.54
-
-
Simplify the Usage of Lexicon in Chinese NER
W2NER
83.08
-
-
Unified Named Entity Recognition as Word-Word Relation Classification
Baseline + BS
82.83
-
-
Boundary Smoothing for Named Entity Recognition
AESINER
81.18
-
-
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
CAN-NER Model
73.64
75.05
72.29
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
BERT-MRC
82.11
-
-
A Unified MRC Framework for Named Entity Recognition
FLAT+BERT
81.82
-
-
FLAT: Chinese NER Using Flat-Lattice Transformer
FGN
82.04
-
-
FGN: Fusion Glyph Network for Chinese Named Entity Recognition
BERT-MRC+DSC
84.47
-
-
Dice Loss for Data-imbalanced NLP Tasks
Glyce + BERT
80.62
81.87
81.4
Glyce: Glyph-vectors for Chinese Character Representations
0 of 15 row(s) selected.
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