Abstract
Traditional Chinese documents classifiers are based on keywords in the documents, which need dictionaries support and efficient segmentation procedures. This paper explores the techniques of utilizing N-gram information to categorize Chinese documents so that the classifier can shake off the burden of large dictionaries and complex segmentation processing, and subsequently be domain and time independent. A Chinese documents classification system following above described techniques is implemented with Naive Bayes, kNN and hierarchical classification methods. Experimental results show that our system can achieve satisfactory performance, which is comparable with other traditional classifiers.
This work was supported by China Post-doctoral Science Foundation and the Natural Science Foundation of China (NSFC) under grant number 60173027.
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© 2002 Springer-Verlag Berlin Heidelberg
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Zhou, S., Guan, J. (2002). Chinese Documents Classification Based on N-Grams. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2002. Lecture Notes in Computer Science, vol 2276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45715-1_43
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DOI: https://doi.org/10.1007/3-540-45715-1_43
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