Combining Concept Graph with Improved Neural Networks for Chinese Short Text Classification

  • Jialu Liao
  • Fanke Sun
  • Jinguang GuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1157)


With the development of the Internet, network information is booming, and a large amount of short text data has brought more timely and comprehensive information to people. How to find the required information quickly and accurately from these pieces of information is the focus of the industry. Short text processing is one of the key technologies. Because of the sparse and noisy features of short texts, the traditional classification method can not provide good support. At present, the research on short text classification mainly focuses on two aspects: feature processing and classification algorithm. Most feature processing methods only use text literal information when performing feature expansion, which lacks the ability to discriminate the polysemy that is common in Chinese. In the classification algorithm, there are also problems such as insufficient input characteristics and insufficient classification effect. In order to improve the accuracy of Chinese short text classification, this paper proposes a method of Chinese short text classification based on improved convolutional recurrent neural network and concept graph, which achieves better classification results than existing algorithms.


Chinese short text classification Concept graph Feature processing Deep learning 



This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 61673304 and U1836118, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189.


  1. 1.
    Zhou, T., Chen, M., Yu, J., Terzopoulos, D.: Attention-based natural language person retrieval. In: Computer Vision and Pattern Recognition Workshops (2017)Google Scholar
  2. 2.
    Yu, B., Zhang, L., School of Management: Chinese short text classification based on CP-CNN. Appl. Res. Comput. 35, 1001–1004 (2018)Google Scholar
  3. 3.
    Zhang, D., Xu, H., Su, Z., Xu, Y.: Chinese comments sentiment classification based on word2vec and SVM\(^{perf}\). Expert Syst. Appl. 42(4), 1857–1863 (2014)Google Scholar
  4. 4.
    Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014)Google Scholar
  5. 5.
    Li, S., Yan, Z., Wu, X., Li, A., Zhou, B.: A method of emotional analysis of movie based on convolution neural network and bi-directional LSTM RNN. In: 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), pp. 156–161 (2017)Google Scholar
  6. 6.
    Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)Google Scholar
  7. 7.
    Zhang, Y., Er, M.J., Wang, N., Pratama, M.: Attention pooling-based convolutional neural network for sentence modelling. Inf. Sci. Int. J. 373(C), 388–403 (2016)Google Scholar
  8. 8.
    Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: Twenty-Sixth International Joint Conference on Artificial Intelligence (2017)Google Scholar
  10. 10.
    Liang, J., Xiao, Y., Wang, H., Zhang, Y., Wang, W.: Probase+: inferring missing links in conceptual taxonomies. IEEE Trans. Knowl. Data Eng. 29(6), 1281–1295 (2017)CrossRefGoogle Scholar
  11. 11.
    Lu, Z., Liu, W., Zhou, Y., Hu, X., Wang, B.: An effective approach for Chinese news headline classification based on multi-representation mixed model with attention and ensemble learning. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 339–350. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina

Personalised recommendations