Deep Learning Chinese Word Segment
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README.md

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

  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

  1. 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)

  1. 生成vocab

bazel build kcws/cc:dump_vocab

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

  1. 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

  1. 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)

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

  2. 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