Abstract
Distributional Clustering has showed to be an effective and powerful approach to supervised term extraction aimed at reducing the original indexing space dimensionality for Automatic Text Categorization [2]. In a recent paper [1] we introduced a new Signal Processing approach to Distributional Clustering which reached categorization results on 20 Newsgroups dataset similar to those obtained by other information-theoretic approaches [3][4][5] . Here we re-validate our method by showing that the 90-categories Reuters-21578 benchmark collection can be indexed with a minimum loss of categorization accuracy (around 2% with Naïve Bayes categorizer) with only 50 clusters.
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References
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Capdevila Dalmau, M., Márquez Flórez, O.W. (2007). Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_67
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DOI: https://doi.org/10.1007/978-3-540-71496-5_67
Publisher Name: Springer, Berlin, Heidelberg
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