chinese_segmentation's Introduction

Chinese Segmentation

Requirements

data

Raw corpus

people2014.tar.gz

Processed dictionary

people.dict.pkl

python dict_utils.py

Models

Matching Based Methods

python test_matching.py

Hidden Markov Models

python test_hmm.py

Conditional Random Field

BiGRU + CRF Epoch 37/100 2157/2157 [==============================] - 77s 36ms/step - loss: 4.7686 - accuracy: 0.9585 - val_loss: 5.4757 - val_accuracy: 0.9419

Model: "bi_rnncrf"

Layer (type) Output Shape Param #

embedding (Embedding) multiple 253184

bidirectional (Bidirectional multiple 18816

dense (Dense) multiple 165

crf (CRF) multiple 25

Total params: 272,190 Trainable params: 272,190 Non-trainable params: 0

None

Train done! Lasts: 3253.28s

BiLSTM + Attention

Epoch 32/100 2157/2157 [==============================] - 68s 31ms/step - loss: 0.1030 - acc: 0.7230 - val_loss: 0.1610 - val_acc: 0.7040

Model: "bi_rnn_attention"

Layer (type) Output Shape Param #

embedding (Embedding) multiple 253184

bidirectional (Bidirectional multiple 18816

attention (Attention) multiple 3168

dense (Dense) multiple 165

Total params: 275,333 Trainable params: 275,333 Non-trainable params: 0

None

Train done! Lasts: 2578.73s

Performance

model	precision	recall	f1_score	note
bimm	0.9121	0.9536	0.9324
hmm	0.6921	0.6899	0.6910
jieba	0.8151	0.8092	0.8122
bilstm	0.8849	0.8701	0.8774
bigru+crf	0.8857	0.8760	0.8808	epochs=37
bilstm+attention	0.8439	0.7701	0.8053	epoch is about 10, not converge yet
bilstm+attention	0.8581	0.7850	0.8199	epochs=32
BERT
BERT+CRF

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