Abstract
Text categorization is a crucial task of increasing importance. Our work focuses on the study of Chinese text categorization on the basis of Boosting model. We chose the People’s Daily news from TREC5 as our benchmark datasets. A minor modification to AdaBoost algorithm (Freund and Schapire, 1996, 2000) was applied for this hypothesis. By way of using the F1 measure for its final evaluation, the results of the Boosting model (AdaBoost.MH) is proved to be effective and outperforms most of other algorithms reported for Chinese text categorization.
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Geng, Y., Zhu, G., Qiu, J., Fan, J., Zhang, J. (2004). An Experimental Study of Boosting Model Classifiers for Chinese Text Categorization. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, Ep. (eds) Digital Libraries: International Collaboration and Cross-Fertilization. ICADL 2004. Lecture Notes in Computer Science, vol 3334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30544-6_29
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DOI: https://doi.org/10.1007/978-3-540-30544-6_29
Publisher Name: Springer, Berlin, Heidelberg
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