Abstract
Automatic text classification is a research focus and core technology in natural language processing and information retrieval. The class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse; its classification accuracy is not high and it lacks semantic information. To overcome these problems, this paper proposes an improved class-center method for text classification using dependencies and the WordNet dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on a large corpus.
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Acknowledgements
This work has been supported by the Natural Science Foundation of Guangxi of China under the contract number 2018GXNSFAA138087, the National Natural Science Foundation of China under the contract numbers 61462010 and 61363036, and Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.
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Zhu, X., Xu, Q., Chen, Y., Wu, T. (2019). An Improved Class-Center Method for Text Classification Using Dependencies and WordNet. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_1
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DOI: https://doi.org/10.1007/978-3-030-32236-6_1
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