关键词: 短文本分类, 卷积神经网络, 双向长短时记忆网络, 注意力机制

Abstract:

Short text is usually composed of several to dozens of words. Short length and sparse features make it difficult to improve the classification accuracy of short texts. In order to solve this problem, an algorithm of classification for Chinese short texts is proposed based on local semantic features and context relationships, called Bi-LSTM_CNN_AT. In this algorithm, CNN is utilized to extract the local semantic features of a text, while Bi-LSTM is used to extract the contextual semantic features of the text. Moreover, the attention mechanism is combined too. Thus, the Bi-LSTM_CNN_AT model is able to extract the most relevant features to the current task from short texts. The experimental results show that the Bi-LSTM_CNN_AT model achieves a classification accuracy of 81.31% in the 18 categories of NLP&CC2017 news headline classification dataset, which is 2.02% higher than the single-channel CNN model and 1.77% higher than the single-channel Bi-LSTM model respectively.

Key words: short text classification, convolutional neural network, bidirectional long short-term memory network, attention mechanism