Abstract
Webpage categorization has turned out to be an important topic in recent years. In a webpage, text is usually the main content, so that auto text categorization (ATC) becomes the key technique to such a task. For Chinese text categorization as well as Chinese webpage categorization, one of the basic and urgent problems is the construction of a good benchmark corpus. In this study, a machine learning approach is presented to refine a corpus for Chinese webpage categorization, where the AdaBoost algorithm is adopted to identify outliers in the corpus. The standard k nearest neighbor (kNN) algorithm under a vector space model (VSM) is adopted to construct a webpage categorization system. Simulation results as well as manual investigation of the identified outliers reveal that the presented method works well.
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Luo, D., Wang, X., Wu, X., Chi, H. (2005). Learning Outliers to Refine a Corpus for Chinese Webpage Categorization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_19
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DOI: https://doi.org/10.1007/11539087_19
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