Abstract
This paper presents the framework and methodologies of Soochow university team’s news headline classification system for NLPCC 2017 shared task 2. The submitted systems aim to automatically classify each Chinese news headline into one or more predefined categories. We develop a voting system based on convolutional neural networks (CNN), gated recurrent units (GRU), and support vector machine (SVM). Experimental results show that our method achieves a Macro-F1 score of about 81%, outperforming most strong competitors, and ranking at 6th in the 32 participants.
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Acknowledgments
This research work is supported by National Natural Science Foundation of China (Grants No. 61373097, No. 61672367, No. 61672368, No. 61331011, No. 61773276), the Research Foundation of the Ministry of Education and China Mobile, MCM20150602 and the Science and Technology Plan of Jiangsu, SBK2015022101 and BK20151222. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.
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Zhu, F., Dong, X., Song, R., Hong, Y., Zhu, Q. (2018). A Multiple Learning Model Based Voting System for News Headline Classification. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_69
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DOI: https://doi.org/10.1007/978-3-319-73618-1_69
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