A text classification method based on capsule network

Abstract

As an important research topic, text classification is often deeply studied by scholars. Convolutional neural network is a commonly used deep learning algorithm for text classification. However, CNN will lose some information features in the pooling process due to the limitation of fixed-size convolution kernel when extracting features from complex semantic information. In order to avoid CNN's problems in text classification tasks, Capsule network is used to extract text features. CapsNet model has the problem of redundant information transmission. In order to reduce redundant information, a capsule screening network is proposed in this paper to remove the weak connection between the screened child capsule and the parent capsule, optimize the dynamic routing algorithm, and reduce the redundant information transmission of the capsule layer. In addition, in the classification of text, the text may exist, such as less useful information, serious colloquial language, unable to reflect the importance of words and so on. In order to improve this problem, this paper introduces the attention model. Combined with improved CapsNet to handle text sorting tasks. Three experimental results show that CapsNet with improved mechanism of attention is the best in text classification task.