Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (11): 173-178.DOI: 10.3778/j.issn.1002-8331.2002-0127

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Text Categorization Method Based on Word Co-occurrence and Graph Convolution

SHEN Yanguang, JIA Yaoqing   

  1. 1.College of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
    2.Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan, Hebei 056038, China
  • Online:2021-06-01 Published:2021-05-31



  1. 1.河北工程大学 信息与电气工程学院,河北 邯郸 056038
    2.河北工程大学 河北省安防信息感知与处理重点实验室,河北 邯郸 056038


Aiming at the problem of small number of labels in text classification tasks, a semi-supervised text classification method based on the combination of word co-occurrence and graph convolutional neural networks is proposed. The model uses the word co-occurrence method to count the word co-occurrence information of the words in the corpus, and filters the word co-occurrence information to build a text graph containing a large graph structure of word nodes and document nodes. The feature matrix is input to a graph convolutional neural network combined with attention mechanism to implement text classification. The experimental results show that compared with current multiple text categorization algorithms, this method has achieved better results on the classic data sets 20NG, Ohsumed and MR.

Key words: text categorization, word co-occurrence, Graph Convolutional Network(GCN)



关键词: 文本分类, 词共现, 图卷积神经网络