通信学报2017，Vol.38Issue(4)：86-98,13.DOI:10.11959/j.issn.1000-436x.2017088
基于神经网络的文本表示模型新方法
New method of text representation model based on neural network
曾谁飞 1张笑燕 1杜晓峰 2陆天波1
作者信息
- 1. 北京邮电大学软件学院,北京100876
- 2. 北京邮电大学计算机学院,北京100876
- 折叠
摘要
Abstract
Method of text representation model was proposed to extract word-embedding from text feature.Firstly,the word-embedding of the dual word-embedding list based on dictionary index and the corresponding part of speech index was created.Then,feature vectors was obtained further from these extracted word-embeddings by using Bi-LSTM recurrent neural network.Finally,the sentence vectors were processed by mean-pooling layer and text categorization was classified by softmax layer.The training effects and extraction performance of the combination model of Bi-LSTM and double word-embedding neural network were verified.The experimental results show that this model not only performs well in dealing with the high-quality text feature vector and the expression sequence,but also significantly outperforms other three kinds of neural networks,which includes LSTM,LSTM+context window and Bi-LSTM.关键词
神经网络/词向量/Bi-LSTM/文本表示Key words
neural network/word-embedding/Bi-LSTM/text representation分类
信息技术与安全科学引用本文复制引用
曾谁飞,张笑燕,杜晓峰,陆天波..基于神经网络的文本表示模型新方法[J].通信学报,2017,38(4):86-98,13.
