关键词: 文本分类, 长短时记忆网络（LSTM）, 注意力机制, 卷积神经网络（CNN）, 特征融合

Abstract:

For the problem that traditional Long Short-Term Memory（LSTM） and Convolution Neural Network（CNN） cannot reflect the importance of each word in the text when extracting features, a text classification method based on the hybrid model of LSTM-Attention and CNN is proposed. Firstly, CNN is used to extract the local information of the text and then integrate the semantics of the whole text. Secondly, LSTM is used to extract text context features. After LSTM, Attention mechanism is added to extract the Attention score of output information. Finally, the output of LSTM-Attention is fused with the output of CNN, so as to realize the effective extraction of text features and focus Attention on important words. The experimental results on three open data sets show that the proposed model is more effective than LSTM, CNN and their improved models, and can effectively improve the effect of text classification.

Key words: text classification, Long Short-Term Memory（LSTM）, attention mechanism, Convolution Neural Network（CNN）, feature fusion