hankcs / Id Cnn Cws
Licence: gpl-3.0
Source codes and corpora of paper "Iterated Dilated Convolutions for Chinese Word Segmentation"
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ID-CNN-CWS
Source codes and corpora of paper "Iterated Dilated Convolutions for Chinese Word Segmentation".
It implements the following 4 models for CWS:
- Bi-LSTM
- Bi-LSTM-CRF
- ID-CNN
- ID-CNN-CRF
Dependencies
- Python >= 3.6
- TensorFlow >= 1.2
Both CPU and GPU are supported. GPU training is 10 times faster.
Preparation
Run following script to convert corpus to TensorFlow dataset.
$ ./scripts/make.sh
Train and Test
Quick Start
$ ./scripts/run.sh $dataset $model
-
$datasetcan bepku,msr,asSCorcityuSC. -
$modelcan becnnorbilstm.
For example:
$ ./scripts/run.sh pku cnn
It will train a cnn model on pku dataset, then evaluate performance on test set.
CRF Layer
To enable CRF layer, simply append --viterbi to your command, e.g.
$ ./scripts/run.sh pku cnn --viterbi
Accuracy
Speed
Acknowledgments
- Corpora are from SIGHAN05, converted to Simplified Chinese via HanLP. Note that the SIGHAN datasets should only be used for research purposes.
- Model implementations adopted from https://github.com/iesl/dilated-cnn-ner by Emma Strubell.
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