Abstract
In this paper, we examine the use of keywords in text categorization with SVM. In contrast to the usual belief, we reveal that using keywords instead of all words yields better performance both in terms of accuracy and time. Unlike the previous studies that focus on keyword selection metrics, we compare the two approaches for keyword selection. In corpus-based approach, a single set of keywords is selected for all classes. In class-based approach, a distinct set of keywords is selected for each class. We perform the experiments with the standard Reuters-21578 dataset, with both boolean and tf-idf weighting. Our results show that although tf-idf weighting performs better, boolean weighting can be used where time and space resources are limited. Corpus-based approach with 2000 keywords performs the best. However, for small number of keywords, class-based approach outperforms the corpus-based approach with the same number of keywords.
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References
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398. Springer, Heidelberg (1998)
Özgür, L., Güngör, T., Gürgen, F.: Adaptive Anti-Spam Filtering for Agglutinative Languages. A Special Case for Turkish. Pattern Recognition Letters 25(16), 1819–1831 (2004)
McCallum, A., Nigam, K.: A Comparison of Event Models for Naïve Bayes Text Classification. In: Sahami, M. (ed.) Proc. of AAAI Workshop on Learning for Text Categorization, Madison, WI, pp. 41–48 (1998)
Yang, Y., Liu, X.: A Re-examination of Text Categorization Methods. In: Proceedings of SIGIR 1999, 22nd ACM International Conference on Research and Development in Information Retrieval, Berkeley, US (1996)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(5), 1–47 (2002)
Forman, G.: An Extensive Empirical Study of Feature Selection Metrics for Text Classification. Journal of Machine Learning Research 3, 1289–1305 (2003)
Özgür, A.: Supervised and Unsupervised Machine Learning Techniques for Text Document Categorization. Master’s Thesis. Bogazici University, Turkey (2004)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Joachims, T.: Advances in Kernel Methods-Support Vector Learning. In: Making Large-Scale SVM Learning Practical. MIT-Press, Cambridge (1999)
Lin, S.-H., Shih, C.-S., Chen, M.C., Ho, J.-M.: Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach. In: Proc. of ACM/SIGIR, Melbourne, Australia, pp. 241–249 (1998)
Azcarraga, A.P., Yap, T., Chua, T.S.: Comparing Keyword Extraction Techniques for Websom Text Archives. International Journal of Artificial Intelligence Tools 11(2) (2002)
Aizawa, A.: Linguistic Techniques to Improve the Performance of Automatic Text Categorization. In: Proceedings of 6th Natural Language Processing Pacific Rim Symposium, Tokyo, JP, pp. 307–314 (2001)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420 (1997)
Mladenic, D., Grobelnic, M.: Feature Selection for Unbalanced Class Distribution and Naive Bayes. In: Proceedings of the 16th International Conference on Machine Learning, pp. 258–267 (1999)
Salton, G., Yang, C., Wong, A.: A Vector-Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)
Porter, M.F.: An Algorithm for Suffix Stripping. Program 14, 130–137 (1980)
Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5), 513–523 (1988)
Lewis, D.D.: Reuters-21578 Document Corpus V1.0, http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
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Özgür, A., Özgür, L., Güngör, T. (2005). Text Categorization with Class-Based and Corpus-Based Keyword Selection. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_63
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DOI: https://doi.org/10.1007/11569596_63
Publisher Name: Springer, Berlin, Heidelberg
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