supercoderhawk / Deeplearning_nlp
Licence: mit
基于深度学习的自然语言处理库
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基于深度学习的自然语言处理库
本项目是对DeepNLP的重构,着重增强架构设计的合理性,提高代码的可读性,减少模块的耦合度,并增加一些新功能。
环境
python >= 3.5
tensorflow >= 1.3.0
sklearn
scipy
项目结构
本项目的核心代码位于python\dnlp
目录下
python/dnlp
│ cws.py # 分词
│ ner.py # 命名实体识别
│ rel_extract.py # 关系抽取
│ __init__.py
│
├─config
│ config.py # 配置项
│ __init__.py
│
├─core # 核心功能模块
│ │ dnn_crf.py # 基于dnn-crf的序列标注
│ │ dnn_crf_base.py # 基于dnn-crf的序列标注的基类
│ │ mmtnn.py # max-margin tensor nural network模型
│ │ re_cnn.py # 基于cnn的关系抽取
│ │ __init__.py
│
├─data_process # 训练和测试数据的预处理
│ processor.py # 基类
│ process_cws.py # 对分词的预处理
│ process_emr.py
│ process_ner.py # 对命名实体识别的预处理
│ process_pos.py # 对词性标注的预处理
│ __init__.py
│
│
├─models # 保存训练后的模型
│
├─scripts # 运行脚本,包括初始化数据集和训练测试等等
│ init_datasets.py # 初始化训练数据
│ cws_ner.py # 进行分词和命名实体识别的训练和使用
│ __init__.py
│
├─tests # 单元测试
├─utils # 公用函数
constant.py # 一些常量
__init__.py
运行
- 初始化数据
python python\scripts\init_datasets.py
- 训练
python python\scripts\cws_ner.py -t
- 使用
python python\scripts\cws_ner.py -p
参考论文
中文分词 && 命名实体识别
-
deep learning for chinese word segmentation and pos tagging (完全实现,文件
dnn_crf.py
) -
Long Short-Term Memory Neural Networks for Chinese Word Segmentation (完全实现,文件
dnn_crf.py
) -
Max-Margin Tensor Neural Network for Chinese Word Segmentation (待实现,文件
mmtnn.py
)
实体关系抽取
ToDo-List
- [ ] 完善文档
- [ ] 增加更多算法的实现
- [ ] 支持pip
- [ ] 加入TensorBoard支持
- [ ] 支持TensorFlow Estimator和Save Model
- [ ] 增加对Java、C++的支持
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