Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
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In general, text has multiple topics. Thus, automatic topic detection from text is harder than the traditional pattern classification tasks because multiple categories must be considered in text categorization. Since the conventional methods do not consider a generative model of multicategory text, they have an important limitation when applied to the multicategory detection problem. In this paper, we propose new probabilistic generative models, parametric mixture models (PMM1 and PMM2), and then present a method for simultaneously detecting multiple topics from text using PMMs. In PMMs, all multitopic classes can be completely represented by basis vectors each of which corresponds to a single-topic class. Moreover, the global optimality of estimated parameter values is theoretically guaranteed in PMM1. Furthermore, parameter estimation and topic detection algorithms are quite efficient. We also empirically show the usefulness of our method through multitopic categorization of World Wide Web pages, focusing on those from the “” domain. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(2): 56–66, 2006; Published online in Wiley InterScience (). DOI 10.1002/scj.20259