Abstract
This paper reports a system that hierarchically classifies Chinese web documents without dictionary support and segmentation procedure. In our classifier, Web documents are represented by N-grams (N≤4) that are easy to be extracted. A boosting machine learning approach is applied to classifying Web Chinese documents that share a topic hierarchy. The open and modularized system architecture makes our classifier be extendible. Experimental results show that our system can effectively and efficiently classify Chinese Web documents.
This work is supported by the 973 High-Tech Projects Foundation of China and partially supported by a grant (No. 69933010) from NSFC.
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Zhou, S., Fan, Y., Hu, J., Yu, F., Hu, Y. (2000). Hierarchically Classifying Chinese Web Documents without Dictionary Support and Segmentation Procedure1 . In: Lu, H., Zhou, A. (eds) Web-Age Information Management. WAIM 2000. Lecture Notes in Computer Science, vol 1846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45151-X_20
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DOI: https://doi.org/10.1007/3-540-45151-X_20
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