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Supervised feature extraction for text categorization

Abstract : This paper concerns finding the `optimal' number of (non-overlapping) word groups for text classification. We present a method to select which words to cluster in word groups and how many such word groups to use on the basis of a set of pre-classified texts. The method involves a `greedy' search through the space of possible word groups. The words are grouped according to the `Jensen-Shannon divergence' between the corresponding distributions over the classes. The criterion to decide which number of word groups to use is based on Rissanen's MDL Principle. We present empirical results that indicate that the proposed method performs well. Furthermore, the proposed method outperforms cross-validation in the sense that far fewer word groups are selected while prediction accuracy is just slightly worse. For the experimentation we used a subset of the `20 Newsgroup' data set.
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Contributor : Jakob Verbeek Connect in order to contact the contributor
Submitted on : Wednesday, February 16, 2011 - 4:59:44 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on: : Tuesday, May 17, 2011 - 2:39:31 AM


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  • HAL Id : inria-00321520, version 1


Jakob Verbeek. Supervised feature extraction for text categorization. Tenth Belgian-Dutch Conference on Machine Learning (Benelearn '00), Dec 2000, Tilburg, Netherlands. ⟨inria-00321520⟩



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