Abstract
The distribution of the number of documents in topic classes is typically highly skewed. This leads to good micro-average performance but not so desirable macro-average performance. By viewing topics as clusters in a high dimensional space, we propose the use of clustering to determine subtopic clusters for large topic classes by assuming that large topic clusters are in general a mixture of a number of subtopic clusters. We used the Reuters News articles and support vector machines to evaluate whether using subtopic cluster can lead to better macro-average performance.
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Chik, F.C.Y., Luk, R.W.P., Chung, K.F.L. (2005). Text Categorization Based on Subtopic Clusters. In: Montoyo, A., Muńoz, R., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2005. Lecture Notes in Computer Science, vol 3513. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428817_19
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DOI: https://doi.org/10.1007/11428817_19
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