A short text classification method based on convolutional neural network and semantic extension

Abstract

In order to solve the problem that traditional short text classification methods do not perform well on short text due to the data sparsity and insufficient semantic features, we propose a short text classification method based on convolutional neural network and semantic extension. Firstly, we propose an improved similarity to improve the coverage of the word vector table in the short text preprocessing process. Secondly, we propose a method for semantic expansion of short texts, which adding an attention mechanism to the neural network model to find related words in the short text, and semantic expansion is performed at the sentence level and the related word level of the short text respectively. Finally, the feature extraction of short text is carried out by means of the classical convolutional neural network. The experimental results show that the proposed method is feasible during the classification task of short text, and the classification effectiveness is significantly improved.