Computer Science ›› 2022, Vol. 49 ›› Issue (8): 205-216.doi: 10.11896/jsjkx.210800064

• Artificial Intelligence • Previous Articles     Next Articles

Review of Text Classification Methods Based on Graph Convolutional Network

TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo   

  1. Key Laboratory of Embedded System and Service Computing(Tongji University),Ministry of Education,Shanghai 201804,China
  • Received:2021-08-09 Revised:2021-10-19 Published:2022-08-02
  • About author:TAN Ying-ying,born in 1998,postgra-duate.Her main research interests include natural language processing and deep learning.
    WANG Jun-li,born in 1978,Ph.D,associate researcher.Her main research interests include text data analysis,deep learning and artificial intelligence.
  • Supported by:
    National Key Research and Development Project of China(2017YFA0700602) and National Natural Science Foundation of China(61672381).

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.

Key words: Graph attention network, Graph convolutional network, Non-Euclidean space, Over-smoothing, Text classification

CLC Number: 

  • TP391.1
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