Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification

Abstract

With the wide application of graph neural network technology in the field of natural language processing,the research of text classification based on graph neural networks has received more and more attention.Building graph for text is an important research task in the application of graph neural networks for text classification.Existing methods cannot effectively capture the dependency of long-distance words in sentences when building graph.Short text classification is a special type of text classification task in which the classified text is generally short,so the traditional text representation is usually sparse and lacks rich semantic information.Based on this,in this paper we propose a short text classification method based on graph convolutional neural networks incorporating long-distance words dependency.Firstly,by using the co-occurrence relationship of words,the containment relationship between documents and words,and the long-distance words dependency in sentences,a text graph is constructed for the entire text corpus.Then,the text graph is input into the graph convolutional neural networks,and the category label prediction is made for each document node after 2-layer convolution.The experimental results on the three datasets of online_shopping_10_cats,summaries of Chinese papers and hotel reviews show that the proposed method achieves better results than the existing baselines.

Keywords

• short text classification|graph convolutional neural network|syntactic relationship|building graph for text|natural language processing