Tensor Graph Convolutional Networks for Text Classification


  • Xien Liu Tsinghua University
  • Xinxin You iFlytek Research
  • Xiao Zhang Tsinghua University
  • Ji Wu Tsinghua University
  • Ping Lv IFLYTEK Co., Ltd




Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.




How to Cite

Liu, X., You, X., Zhang, X., Wu, J., & Lv, P. (2020). Tensor Graph Convolutional Networks for Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8409-8416. https://doi.org/10.1609/aaai.v34i05.6359



AAAI Technical Track: Natural Language Processing