Affiliations: [a] School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China | [b] Information and Communication Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China | [c] Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China
Correspondence:
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Corresponding author: Xinyu He, School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China. E-mail: [email protected].
Abstract: Due to the vigorous development of big data, news topic text classification has received extensive attention, and the accuracy of news topic text classification and the semantic analysis of text are worth us to explore. The semantic information contained in news topic text has an important impact on the classification results. Traditional text classification methods tend to default the text structure to the sequential linear structure, then classify by giving weight to words or according to the frequency value of words, while ignoring the semantic information in the text, which eventually leads to poor classification results. In order to solve the above problems, this paper proposes a BiLSTM-GCN (Bidirectional Long Short-Term Memory and Graph Convolutional Network) hybrid neural network text classification model based on dependency parsing. Firstly, we use BiLSTM to complete the extraction of feature vectors in the text; Then, we employ dependency parsing to strengthen the influence of words with semantic relationship, and obtain the global information of the text through GCN; Finally, aim to prevent the overfitting problem of the hybrid neural network which may be caused by too many network layers, we add a global average pooling layer. Our experimental results show that this method has a good performance on the THUCNews and SogouCS datasets, and the F-score reaches 91.37% and 91.76% respectively.
Keywords: Text classification, semantic information, dependency graph structure, BiLSTM, GCN
