Abstract
XSLT is a transformation language mainly used for converting XML documents to HTML or other formats. Due to its simplicity and flexibility XML has replaced traditional EDI file formats. Most e-business applications store data in XML, convert XML into HTML using XSTL, and publish the HTML documents to the web. In this paper we argue that the use of XSLT presents an opportunity rather than a challenge to web document classification. We show that it is possible to combine the advantages of both HTML and XML into classification of documents at the XSLT transformation stage, named XSLT classification, to attain higher classification rates using Support Vector Machines (SVM). The results are both expected and promising. We believe that XSLT classification can become a favorable classification method over HTML or XML classification where XSLT stylesheets are available.
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References
Tozal, E.: Classification Using XSLT. MS Thesis, Comp. Eng.Fatih University (2005)
Kurt, A., Tozal, E.: A Web Classification Framework Based on XSLT. In: Shen, H.T., Li, J., Li, M., Ni, J., Wang, W. (eds.) APWeb Workshops 2006. LNCS, vol. 3842, pp. 86–96. Springer, Heidelberg (2006)
Dumais, S., et al.: Inductive learning algorithms and representations for text categ-orization. In: 7th Int. Conf. on Information and knowledge management, pp. 148–155 (1998)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: 10th European Conference on Machine Learning (ECML) (1998)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)
Basu, A., Watters, C., Shepherd, M.: Support Vector Machines for Text Categorization. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences (2003)
Mladenic, D.: Turning Yahoo to Automatic Web-Page Classifier. In: European Conference on Artificial Intelligence (1998)
Esposto, F., Malerba, D., Pace, L.D., Leo, P.: A machine learning apporach to web mining. In: Proc. of the 6th Congress of the Italian Association for Artificial Intelligence (1999)
Sun, A., Lim, E., Ng, W.: Web classification using support vector machine. In: The 4th Int. Workshop on Web information and Data Management, ACM Press, New York (2002)
Asirvatham, A.P., Ravi, K.K.: Web Page Classification based on Document Structure (2001)
Oh, H.-J., et al.: A practical hypertext categorization method using links and incrementally available class information. In: The 23rd ACM Int. Conf. on R & D in Information Retrieval (2000)
Chakrabarti, S., Dom, B.E., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of the ACM SIGMOD (1998)
Yi, J., Sundaresan, N.: A classifier for semi-structured documents. In: Proceedings of the 6th ACM SIGKDD 2000 (2000)
Denoyer, L., Gallinari, P.: Bayesian network model for semi-structured document classification. Information Processing and Management 40(5) (2004)
Lewis, D.D.: Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, Springer, Heidelberg (1998)
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence (2001)
McCallum, A., Nigam, K.: A comparision of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)
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Kurt, A., Tozal, E. (2006). Classification of XSLT-Generated Web Documents with Support Vector Machines. In: Nayak, R., Zaki, M.J. (eds) Knowledge Discovery from XML Documents. KDXD 2006. Lecture Notes in Computer Science, vol 3915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11730262_6
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DOI: https://doi.org/10.1007/11730262_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33180-3
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