Abstract
Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related Web sites all provide rich information for classifying hypertext documents. How to appropriately represent that information and automatically learn statistical patterns for solving hypertext classification problems is an open question. This paper seeks a principled approach to providing the answers. Specifically, we define five hypertext regularities which may (or may not) hold in a particular application domain, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three well-known learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare alternative ways to exploit them. Our results show that the identification of hypertext regularities in the data and the selection of appropriate representations for hypertext in particular domains are crucial, but seldom obvious, in real-world problems. We find that adding the words in the linked neighborhood to the page having those links (both inlinks and outlinks) were helpful for all our classifiers on one data set, but more harmful than helpful for two out of the three classifiers on the remaining datasets. We also observed that extracting meta data from related Web sites was extremely useful for improving classification accuracy in some of those domains. Finally, the relative performance of the classifiers being tested provided insights into their strengths and limitations for solving classification problems involving diverse and often noisy Web pages.
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References
Chakrabarti, S., Dom, B.E., and Indyk, P. (1998). Enhanced Hypertext Categorization using Hyperlinks. In L.M. Haas and A. Tiwary (Eds.), Proceedings of SIGMOD-98, ACM International Conference on Management of Data, Seattle (pp. 307–318). New York, US: ACM Press.
Chen, H. and Dumais, S.T. (2000). Bringing Order to the Web: Automatically Categorizing Search Results. In Proceedings of CHI-00, ACM International Conference on Human Factors in Computing Systems, Den Haag, NL (pp. 145–152). New York, US: ACM Press.
Cohen, W. (1995). Learning to Classify English Text with ILP Methods. In L. De Raedt (Ed.), Advances in Inductive Logic Programming. Amsterdam: IOS Press.
Cohen, W. (2000). Automatically Extracting Features for Concept Learning from the Web. In Seventeenth International Conference on Machine Learning.
Cohen, W.W. (1995). Learning to Classify English Text with ILP Methods. In L. De Raedt (Ed.), Advances in Inductive Logic Programming (pp. 124–143). Amsterdam: IOS Press.
Craven, M., DiPasquo, D., Freitag, D., McCallum, A.K., Mitchell, T.M., Nigam, K., and Slattery, S. (2000). Learning to Construct Knowledge Bases from the World Wide Web. Artificial Intelligence, 118(1/2), 69–113.
Craven, M., Slattery, S., and Nigam, K. (1998). First-Order Learning forWeb Mining. In Tenth European Conference on Machine Learning.
Dasarathy, B.V. (1991). Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, McGraw-Hill Computer Science Series. Las Alamitos, CA: IEEE Computer Society Press.
Freitag, D. (1998). Multistrategy Learning for Information Extraction. In Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco (pp. 161–169). San Mateo, CA: Morgan Kaufmann.
Fürnkranz, J. (1999). Exploiting Structural Information for Text Classification on the WWW. In D.J. Hand, J.N. Kok, and M.R. Berthold (Eds.), Proceedings of IDA-99, 3rd Symposium on Intelligent Data Analysis, Amsterdam (pp. 487–497). Heidelberg: Springer Verlag. Published in the “Lecture Notes in Computer Science” series, number 1642.
Ghani, R., Jones, R., Mladenic, D., Nigam, K., and Slattery, S. (2000). Data Mining on Symbolic Knowledge Extracted from the Web. In Workshop on Text Mining at the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Ghani, R., Slattery, S., and Yang, Y. (2001). Hypertext Categorization using Hyperlink Patterns and Meta Data. In Proceedings of ICML-01, 18th International Conference on Machine Learning, Williams College, US. San Francisco: Morgan Kaufmann Publishers.
Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In C. Nédellec and C. Rouveirol (Eds.), Proceedings of ECML-98, 10th European Conference on Machine Learning, Chemnitz, Germany (pp. 137–142). Heidelberg: Springer Verlag. Published in the “Lecture Notes in Computer Science” series, number 1398.
Joachims, T., Cristianini, N., and Shawe-Taylor, J. (2001). Composite Kernels for Hypertext Categorisation. In Proceedings of ICML-01, 18th International Conference on Machine Learning, Williams College, US. San Francisco: Morgan Kaufmann Publishers.
Kleinberg, J. (1998). Authoritative Sources in a Hyperlinked Environment. In Proceedings of the Nineth Annual ACM-SIAM Symposioum on Discrete Algorithms.
Lewis, D.D. (1998). Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In C. Nédellec and C. Rouveirol (Eds.), Proceedings of ECML-98, 10th European Conference on Machine Learning, Chemnitz, (pp. 4–15), Heidelberg: Springer Verlag. Published in the “Lecture Notes in Computer Science” series, number 1398.
McCallum, A. and Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. In AAAI-98 Workshop on Learning for Text Categorization. Tech. rep. WS-98-05. Menlo Park, CA: AAAI Press.
Oh, H.-J., Myaeng, S.H., and Lee, M.-H. (2000). A Practical Hypertext Categorization Method using Links and Incrementally Available Class Information. In N.J. Belkin, P. Ingwersen, and M.-K. Leong (Eds.), Proceedings of SIGIR-00, 23rd ACM International Conference on Research and Development in Information Retrieval, Athens (pp. 264–271). New York: ACM Press.
Quinlan, J.R. (1990). Learning Logical Definitions from Relations. Machine Learning, 5, 239–266.
Salton, G. and Buckley, C. (1988). Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24, 513–523.
Slattery, S. and Craven, M. (1998). Combining Statistical and Relational Methods for Learning in Hypertext Domains. In Proceedings of the 8th international Conference on Inductive Logic Programming, Madison, WI.
Slattery, S. (2001). Hypertext Classification. Ph.D. thesis, Carnegie Mellon University.
Slattery, S. and Craven, M. (2000). Discovering Test Set Regularities in Relational Domains. In P. Langley (Ed.), Proceedings of ICML-00, 17th International Conference on Machine Learning, Stanford, US. San Francisco: Morgan Kaufmann Publishers.
van Rijsbergen, C.J. (1979). Information Retrieval. London: Butterworths.
Yang, Y. (1994). Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorisation and Retrieval. In W.B. Croft and C.J. van Rijsbergen (Eds.), Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, Dublin (pp. 13–22). Heidelberg: Springer Verlag.
Yang, Y. (1999). An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval, 1(1/2), 69–90.
Yang, Y. (2001). A Study on Thresholding Strategies for Text Categorization. In W.B. Croft et al. (Eds.), Proceedings of SIGIR-2001, 24th ACMInternational Conference on Research and Development in Information Retrieval, New Orleans (pp. 137–145). Louisiana: ACM Press.
Yang, Y., Ault, T., and Pierce, T. (2000). Combining Multiple Learning Strategies for Effective Cross-Validation. In P. Langley (Eds.), Proceedings of ICML-00, 17th International Conference on Machine Learning, Stanford (pp. 1167–1182). San Francisco: Morgan Kaufmann Publishers.
Yang, Y. and Pedersen, J.O. (1997). A Comparative Study on Feature Selection in Text Categorization. In D.H. Fisher (Ed.), Proceedings of ICML-97, 14th International Conference on Machine Learning, Nashville (pp. 412–420). San Francisco: Morgan Kaufmann Publishers.
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Yang, Y., Slattery, S. & Ghani, R. A Study of Approaches to Hypertext Categorization. Journal of Intelligent Information Systems 18, 219–241 (2002). https://doi.org/10.1023/A:1013685612819
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DOI: https://doi.org/10.1023/A:1013685612819