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We have preprocessed all these collections by eliminating stop words and by applying the Porter stemmer [22]. The characteristics given in the above tables for each corpus were obtained after applying this preprocessing phase. The results reported in [19] show that better results can be obtained by using those terms which contribute to a better clustering (not noisy terms), instead of the complete vocabulary. This fact have led us to study this issue in order to apply it to our preprocessed corpora. Up to now, different Feature Selection Techniques (FSTs) have been used in the clustering task. However, clustering abstracts for a narrow domain implies the well known problem of the lackness of training corpora. This led us to use unsupervised term selection techniques instead of supervised ones. Following we describe briefly all the techniques employed in our experiments.

David Pinto 2007-05-08