https://github.com/koth/kcws

Deep Learning Chinese Word Segment
https://github.com/koth/kcws

chinese-text-segmentation deep-learning nlp pos-tagger tensorflow

Last synced: 25 days ago
JSON representation

Deep Learning Chinese Word Segment

• Host: GitHub
• URL: https://github.com/koth/kcws
• Owner: koth
• Created: 2016-11-25T00:17:50.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-18T03:22:58.000Z (over 6 years ago)
• Last Synced: 2024-10-01T13:02:51.464Z (about 1 month ago)
• Topics: chinese-text-segmentation, deep-learning, nlp, pos-tagger, tensorflow
• Language: C++
• Homepage:
• Size: 13.4 MB
• Stars: 2,081
• Watchers: 165
• Forks: 648
• Open Issues: 44
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-bazel - kwcs

README

        

### 引用 

 

本项目模型BiLSTM+CRF参考论文：http://www.aclweb.org/anthology/N16-1030 ,IDCNN+CRF参考论文：https://arxiv.org/abs/1702.02098

### 构建

1. 安装好bazel代码构建工具，安装好tensorflow（目前本项目需要tf 1.0.0alpha版本以上)

2. 切换到本项目代码目录，运行./configure

3. 编译后台服务 

   > bazel build //kcws/cc:seg_backend_api

### 训练

1. 关注待字闺中公众号 回复 kcws 获取语料下载地址：

   

   ![logo](https://github.com/koth/kcws/blob/master/docs/qrcode_dzgz.jpg?raw=true "待字闺中")

   

   

2. 解压语料到一个目录

3. 切换到代码目录，运行:

  > python kcws/train/process_anno_file.py <语料目录> pre_chars_for_w2v.txt

  

  > bazel build third_party/word2vec:word2vec

  

  > 先得到初步词表

  

  > ./bazel-bin/third_party/word2vec/word2vec -train pre_chars_for_w2v.txt -save-vocab pre_vocab.txt -min-count 3

  

  > 处理低频词

  

  > python kcws/train/replace_unk.py pre_vocab.txt pre_chars_for_w2v.txt chars_for_w2v.txt

  > 

  > 训练word2vec

  > 

  > ./bazel-bin/third_party/word2vec/word2vec -train chars_for_w2v.txt -output vec.txt -size 50 -sample 1e-4 -negative 5 -hs 1 -binary 0 -iter 5

  > 

  > 构建训练语料工具

  > 

  > bazel build kcws/train:generate_training

  > 

  > 生成语料

  > 

  > ./bazel-bin/kcws/train/generate_training vec.txt <语料目录> all.txt

  > 

  > 得到train.txt , test.txt文件

  > 

  > python kcws/train/filter_sentence.py all.txt

  

4. 安装好tensorflow,切换到kcws代码目录，运行:

  > python kcws/train/train_cws.py --word2vec_path vec.txt --train_data_path <绝对路径到train.txt> --test_data_path test.txt --max_sentence_len 80 --learning_rate 0.001

  （默认使用IDCNN模型，可设置参数”--use_idcnn False“来切换BiLSTM模型)

  

5. 生成vocab

  > bazel  build kcws/cc:dump_vocab

  

  > ./bazel-bin/kcws/cc/dump_vocab vec.txt kcws/models/basic_vocab.txt

  

6. 导出训练好的模型

 >  python tools/freeze_graph.py --input_graph logs/graph.pbtxt  --input_checkpoint logs/model.ckpt --output_node_names  "transitions,Reshape_7"   --output_graph kcws/models/seg_model.pbtxt

7. 词性标注模型下载  (临时方案，后续文档给出词性标注模型训练，导出等）

   >  从 https://pan.baidu.com/s/1bYmABk 下载pos_model.pbtxt到kcws/models/目录下

8. 运行web service

 >  ./bazel-bin/kcws/cc/seg_backend_api --model_path=kcws/models/seg_model.pbtxt(绝对路径到seg_model.pbtxt>)   --vocab_path=kcws/models/basic_vocab.txt   --max_sentence_len=80

### 词性标注的训练说明：

https://github.com/koth/kcws/blob/master/pos_train.md

### 自定义词典

目前支持自定义词典是在解码阶段，参考具体使用方式请参考kcws/cc/test_seg.cc

字典为文本格式，每一行格式如下:

><自定义词条>\t<权重>

比如：

>蓝瘦香菇	4

权重为一个正整数，一般4以上，越大越重要

 

### demo

http://45.32.100.248:9090/

附： 使用相同模型训练的公司名识别demo:

http://45.32.100.248:18080