Attention-based BILSTM network with part-of-speech features for Chinese text classification

Journal of Hebei University of Science and Technology

Hua XU¹  Kai GAO²  Chengliang GAO² 

[1] Department of Computer Science and Technology, Tsinghua University, Beijing 100084,China;School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China;

关键词: natural language processing;    Chinese text classification;    attention mechanism;    LSTM;    part-of-speech;   

DOI  :  10.7535/hbkd.2018yx05010

来源: DOAJ

【 摘 要 】

The Chinese classification methods based on LSTM can correctly identify the category oftext, but such classification methods mainly focus on learning the text fragments related to the theme without aiming at other aspects of the words in context, especially the implicit feature information contained in the part-of-speech. In order to use the part-of-speech information of words effectively to learn a lot of context-dependent feature information and then improve the performance of text classification, this paper proposes a Chinese classification method combining part-of-speech information, which can easily learn implicit features from words and their part-of-speech.To verify the effectiveness of the attention-based BILSTM model with part-of-speech for Chinese classification tasks, this paper designs a series of comparative experiments and conducts on source-open dataset. The experimental results show that the attention-based BILSTMmodel with part-of-speech has better performance on Chinese classification than some baselines, and it proves that the Chinese classification model proposed in this paper is effective.This indicates that identifying the category of text is not only highly correlated with the semantic information of the words, but also has a great relationship with the information of the words' part-of-speech.

【 授权许可】

Unknown