Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 230-236.doi: 10.11896/jsjkx.210600042

• Artificial Intelligence • Previous Articles     Next Articles

Text Classification Method Based on Information Fusion of Dual-graph Neural Network

YAN Jia-dan, JIA Cai-yan   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China
    Beijing Key Lab of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China
  • Received:2021-06-04 Revised:2021-10-23 Published:2022-08-02
  • About author:YAN Jia-dan,born in 1995,postgra-duate.Her main research interests include natural language processing and text classification.
    JIA Cai-yan,born in 1976,Ph.D,professor,is a member of China Computer Federation.Her main research interests include machine learning,complex network analysis and social computing.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(2019JBZ110).

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Abstract: Graph neural networks are recently applied in text classification tasks.Compared with graph convolution network,the text level graph neural network model based on message passing(MP-GNN) has the advantages of low memory usage and supporting online testing.However,MP-GNN model only builds a lexical graph using the word co-occurrence information, and the obtained information lacks diversity.To address this problem,a text classification method based on information fusion of dual-graph neural network is proposed.Besides preserving the original lexical graph built in MP-GNN,this method constructes the semantic graph based on the cosine similarity between pairs of words,and controls the sparsity of the graph through a threshold,which makes more effective use of the multi-directional semantic information of the text.In addition,the ability of direct fusion and attention mechanism fusion are tested to fuse the text representation learned on lexical graph and semantic graph.Experimental results on 12 datasets(R8,R52 and other datasets commonly used for text classification) show that the proposed model demonstrates an obvious improvement on performance of text classification compared with the SOTA(state-of-the-art) methods TextLevelGNN,TextING and MPAD.

Key words: Attention mechanism, Graph neural network, Information fusion, Natural language processing, Semantic information, Text classification

CLC Number: 

  • TP391
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