Abstract
In the world of text document classification, the most general case is that in which a document can be classified into more than one category, the multi-label problem. This paper investigates the performance of two document classification systems applied to the task of multi-class multi-label document classification. Both systems consider the pattern of co-occurrences in documents of multiple categories. One system is based on a novel sequential data representation combined with a kNN classifier designed to make use of sequence information. The other is based on the “Latent Semantic Indexing” analysis combined with the traditional kNN classifier. The experimental results show that the first system performs better than the second on multi-labeled documents, while the second performs better on uni-labeled documents. Performance therefore depends on the dataset applied and the objective of the application.
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References
Ando, R.K., Lee, L.: Iterative Residual Rescaling: An Analysis and Generalization of LSI. In: Proceedings of the ACM SIGIR 2001, pp. 154–162 (2001)
Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using Linear Algebra for Intelligent Information Retrieval. SIAM Review 37(4), 573–595 (1995)
Brill, E.: A Simple Rule Based Part of Speech Tagger. In: Proceedings of the 3rd Conference on Applied Natural Language Processing, pp. 152–155 (1992)
Deerwester, S., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by Latent Semantic Analysis. Journal of the Society for Information Science 41(6), 391–407 (1990)
Haykin, S.: Neural Networks - A Comprehensive Foundation, 2nd edn. Self-Organizing Maps, ch. 9. Prentice Hall, Englewood Cliffs (1999) ISBN 0-13-273350-1
Luo, X., Zincir-Heywood, A.N.: A Comparison of SOM Based Document Categorization Systems. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1786–1791 (2003)
Papadimitriou, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent Semantic Indexing: A probabilistic Analysis. In: Proceedings of the 17th ACM Symposium on the Principles of Database Systems, pp. 159–168 (1998)
Yang, Y., Liu, X.: A Re-examination of Text Categorization Methods. In: Proceedings of the ACM SIGIR 1999, pp. 42–49 (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Luo, X., Zincir-Heywood, A.N. (2005). Evaluation of Two Systems on Multi-class Multi-label Document Classification. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_17
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DOI: https://doi.org/10.1007/11425274_17
Publisher Name: Springer, Berlin, Heidelberg
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