Abstract
This paper introduces a novel approach for improving the reliability of ICA-based text classifiers, attempting to make the most of the independent components of the text data. In this framework, two issues are adressed: firstly, a relative relevance measure for category assignment is presented. And secondly, a reliability control process is included in the classifier, avoiding the classification of documents belonging to none of the categories defined during the training stage. The experiments have been conducted on a journalistic-style text corpus in Catalan, achieving encouraging results in terms of rejection accuracy. However, similar results are obtained when comparing the proposed relevance measure to the classic magnitude-based technique for category assignment.
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References
Sebastiani, F.: Machine Learning in Automated Text Categorisation. ACM Computing Surveys 34(1), 1–47 (2002)
Isbell, C.-L., Viola, P.: Restructuring Sparse High Dimensional Data for Effective Retrieval. Adv. Neural Information Processing Systems 11, 480–486 (1999)
Kolenda, T., Hansen, L.K., Sigurdsson, S.: Independent Components in Text. In: Girolami, M. (ed.) Advances in Independent Component Analysis, pp. 241–262. Springer, Heidelberg (2000)
Kabán, A., Girolami, M.: Unsupervised Topic Separation and Keyword Identification in Document Collections: a Projection Approach. Dept. of Computing and Information Systems, University of Paisley. Technical Report Nr. 10 (2000)
Kim, Y.-H., Zhang, B.-T.: Document Indexing Using Independent Topic Extraction. In: Proc. of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2001). San Diego, USA, pp. 557–562 (2001)
Kolenda, T.: Clustering text using Independent Component Analysis. Inst. of Informatics and Mathematical Modelling, Tech. University of Denmark. T.R. (2002)
Kolenda, T., Hansen, L.K.: Dynamical Components of Chat. Inst. of Informatics and Mathematical Modelling, Tech. University of Denmark. T.R. (2000)
Bingham, E.: Topic Identification in Dynamical Text by Extracting Minimum Complexity Time Components. In: Proc. of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2001), San Diego, USA, pp. 546–551 (2001)
Bressan, M., Vitrià, J.: Improving Naive Bayes Using Class Conditional ICA. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 1–10. Springer, Heidelberg (2002)
Takamura, H., Matsumoto, Y.: Feature Space Restructuring for SVMs with Application to Text Categorization. In: Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2001), Pittsburgh, USA, pp. 51–57 (2001)
Alías, F., Iriondo, I., Barnola, P.: Multi-domain Text Classification for Unit Selection Text-to-Speech Synthesis. In: Proc. of the 15th International Congress of Phonetic Sciences (ICPhS 2003). Barcelona, Spain, pp. 2341–2344 (2003)
Sevillano, X., Alías, F., Socoró, J.C.: ICA-Based Hierarchical Text Classification for Multi-domain Text-to-Speech Synthesis. In: Proc. of the 29th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2004). Montreal, Quebec, Canada (2004) (to appear)
Deerwester, S., Dumais, S.-T., Furnas, G.-W., Landauer, T.-K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal American Society Information Science 6(41), 391–407 (1990)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons, New York (2001)
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Sevillano, X., Alías, F., Socoró, J.C. (2004). Reliability in ICA-Based Text Classification. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_153
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DOI: https://doi.org/10.1007/978-3-540-30110-3_153
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