Abstract
The exponential growth of the web has led to the necessity to put some order to its content. The automatic classification of web documents into predefined classes, that is hypertext categorization, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and linked neighbourhood all provide rich information for hypertext categorization that is not available in traditional text classification. This paper looks at (i) which extra information hidden in HTML tags and linked neighbourhood pages to take into consideration to improve the classification task, and (ii) how to deal with the high level of noise in linked pages. A hypertext dataset and four well-known learning algorithms (Naive Bayes, K-Nearest Neighbour, Support Vector Machine and C4.5) were used to exploit the enriched text representation. The results showed that the clever use of the information in linked neighbourhood and HTML tags improved the accuracy of the classification algorithms.
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© 2005 Springer-Verlag London Limited
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Benbrahim, H., Bramer, M. (2005). Neighbourhood Exploitation in Hypertext Categorization. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXI. SGAI 2004. Springer, London. https://doi.org/10.1007/1-84628-102-4_19
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DOI: https://doi.org/10.1007/1-84628-102-4_19
Publisher Name: Springer, London
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