Abstract
This paper describes problems with classification and filtration of similar relevant and irrelevant real medical documents from one very specific domain, obtained from the Internet resources. Besides the similarity, the documents are often unbalanced—a lack of irrelevant documents for the training. A definition of similarity is suggested. For the classification, six algorithms are tested from the document similarity point of view. The best results are provided by the back propagation-based neural network and by the radial basis function-based support vector machine.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alexandrov, M., Gelbukh, A., Lozovoi, G.: Chi-square Classifier for Document Categorization. In: Gelbukh, A. (ed.) CICLing 2001. LNCS, vol. 2004, pp. 455–457. Springer, Heidelberg (2001)
Gelbukh, A., Sidorov, G., Guzmán-Arenas, A.: Use of a weighted topic hierarchy for text retrieval and classification. In: Matoušek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds.) TSD 1999. LNCS (LNAI), vol. 1692, pp. 130–135. Springer, Heidelberg (1999)
Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1997)
Porter, M.F.: An Algorithm For Suffix Stripping. Program 14(3), 130–137 (1980)
Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proc. of the 8th Annual Conference on Innovative Applications of Artificial Intelligence, AAAI 1996, Portland, Oregon, August 4-8, pp. 725–730 (1996)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Tong, S., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research 2, 45–66 (2001)
Van Rijsbergen, C.J.: Information Retrieval, 2nd edn., Department of Computer Science, University of Glasgow (1979)
Žižka, J., Bourek, A., Frey, L.: TEA: A Text Analysis Tool for the Intelligent Text Document Filtering. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2000. LNCS (LNAI), vol. 1902, pp. 151–156. Springer, Heidelberg (2000)
Žižka, J., Bourek, A.: Automated Selection of Interesting Medical Text Documents by the TEA Text Analyzer. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 402–404. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hroza, J., Žižka, J., Bourek, A. (2004). Filtering Very Similar Text Documents: A Case Study. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-24630-5_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21006-1
Online ISBN: 978-3-540-24630-5
eBook Packages: Springer Book Archive