Text Classification Based on Graph Neural Networks and Dependency Parsing

Abstract

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 classification method.Mix-GCN model simultaneously considers the relationship between adjacent words in the text and the syntactic dependencies existing between text words,which improves the performance of text classification.On 6 benchmark datasets,compared to 8 well-known text classification methods,experimental results show that Mix-GCN has a good text classification effect.

Keywords

• text classification|graph neural network|dependency parsing|graph classification