Abstract
Maximum entropy provides a reasonable way of estimating probability distributions and has been widely used for a number of language processing tasks. In this paper, we explore the use of different feature selection methods for text categorization using maximum entropy modeling. We also propose a new feature selection method based on the difference between the relative document frequencies of a feature for both relevant and irrelevant classes. Our experiments on the Reuters RCV1 data set show that our own feature selection performs better than the other feature selection methods and maximum entropy modeling is a competitive method for text categorization.
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Cai, J., Song, F. (2008). Maximum Entropy Modeling with Feature Selection for Text Categorization. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_62
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DOI: https://doi.org/10.1007/978-3-540-68636-1_62
Publisher Name: Springer, Berlin, Heidelberg
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