Text Classification Based on Graph Neural Networks and Dependency Parsing

• Published in: Ji suan ji ke xue Vol. 49; no. 12; pp. 293 - 300
• Main Authors: Yang, Xu-hua, Jin, Xin, Tao, Jin, Mao, Jian-fei
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.12.2022; Editorial office of Computer Science
• Subjects: Artificial neural networks; Classification; Data mining; Embedding; Graph neural networks; Natural language processing; Neural networks; Text categorization; text classification|graph neural network|dependency parsing|graph classification
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.220300195

Text classification is a basic and important task in natural language processing.It is widely used in language processing scenarios such as news classification, topic tagging and sentiment analysis.The current text classification models generally do not consider the co-occurrence relationship of text words and the syntactic characteristics of the text itself, thus limiting the effect of text classification.Therefore, a text classification model based on graph convolutional neural network（Mix-GCN） is proposed.Firstly, based on the co-occurrence relationship and syntactic dependency between text words, the text data is constructed into a text co-occurrence graph and a syntactic dependency graph.Then the GCN model is used to perform representation learning on the text graph and syntactic dependency graph, and the embedding vector of the word is obtained.Then the embedding vector of the text is obtained by graph pooling method and adaptive fusion method, and the text classification is completed by the graph class