Review of Text Classification Methods Based on Graph Convolutional Network

Abstract

Text classification is a common task in natural language processing,in which there are a lot of research and progress based on machine learning and deep learning.However,these traditional methods can only process Euclidean spatial data,and cannot express the semantic information of the document effectively.To break the traditional learning mode,many recent studies start to use graphs to represent complicated relationships among entities in the document,and explore graph convolutional neural network for text representation.This paper reviews the text classification methods based on graph convolutional network.Firstly,the background and principle of graph convolutional network are summarized.Then,text classification methods based on graph convolutional network are described in detail according to different types of graph-based networks.Meanwhile,it analyzes the limi-tation of graph convolutional network in the depth of networks,and introduces the latest developments of deep networks in text classification.Finally,the classification performance of models involved in this paper is compared through some experiments,and the challenges and future research direction in this field are discussed.

Keywords

• text classification|graph convolutional network|graph attention network|over-smoothing|non-euclidean space