Abstract
Recently there has been an increasing research interest in short text such as news headline. Due to the inherent sparsity of short text, the current text classification methods perform badly when applied to the classification of news headlines. To overcome this problem, a novel method which enhances the semantic representation of headlines is proposed in this paper. Firstly, we add some keywords extracted from the most similar news to expand the word features. Secondly, we use the corpus in news domain to pre-train the word embedding so as to enhance the word representation. Moreover, Fasttext classifier, which uses a liner method to classify text with fast speed and high accuracy, is adopted for news headline classification. On the task for Chinese news headline categorization in NLPCC2017, the proposed method achieved 83.1% of the F-measure, which got the first rank in 33 teams.
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Acknowledgements
Firstly, we would like to thank Jintao Tang and Ting Wang for their valuable suggestions on the initial version of this paper, which have helped a lot to improve the paper. Secondly, we also want to express gratitudes to the anonymous reviewers for their hard work and kind comments, which will further improve our work in the future. This work was supported by the National Natural Science Foundation of China (No. 61602490).
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Yin, Z., Tang, J., Ru, C., Luo, W., Luo, Z., Ma, X. (2018). A Semantic Representation Enhancement Method for Chinese 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_27
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