• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text


Abstract

Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.


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Cite this article

[IEEE Style]
W. Xu, H. Huang, J. Zhang, H. Gu, J. Yang and G. Gui, "CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text," KSII Transactions on Internet and Information Systems, vol. 13, no. 12, pp. 6080-6096, 2019. DOI: 10.3837/tiis.2019.12.016.

[ACM Style]
Wenhua Xu, Hao Huang, Jie Zhang, Hao Gu, Jie Yang, and Guan Gui. 2019. CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text. KSII Transactions on Internet and Information Systems, 13, 12, (2019), 6080-6096. DOI: 10.3837/tiis.2019.12.016.