Abstract
In this paper, an efficient topic-specific Web text filtering framework is proposed. This framework focuses on blocking some topic-specific Web text content. In this framework, a hybrid feature selection method is proposed, and a high efficient filtering engine is designed. In training, we select features based on CHI statistic and rough set theory, then to construct filter with Vector Space Model. We train our frame with huge datasets, and the result suggests our framework is more effective for the topic-specific text filtering. This framework runs at server such as gateway, and it is more efficient than a client-based system.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, Q., Li, J. (2005). An Efficient Topic-Specific Web Text Filtering Framework. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds) Web Technologies Research and Development - APWeb 2005. APWeb 2005. Lecture Notes in Computer Science, vol 3399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31849-1_16
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DOI: https://doi.org/10.1007/978-3-540-31849-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25207-8
Online ISBN: 978-3-540-31849-1
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