… the effect of the distributional features. The question that we attempt to answer is: are the
distributional features useful for text categorization? For eight features ðTF þ 3 CP features þ 4 …

… a text categorization approach that is based on a combination of feature distributional …
Our feature selection approach employs distributional clustering of words via the recently …

… text categorization method that combines the distributional … , for constructing text classifiers.
The high dimensionality of … task of categorization, for which reason, feature clustering has …

… Three benchmark datasets - Reuters-21578, 20 Newsgroups and WebKB - were
experimented with in our application of feature selection for text categorization. In this section we …

… features of a word used in previous text categorization work. A thorough review of text
categorization … Moschitti and Basili [9] has studied the effect of two kinds of linguistic features, POS-…

… features are synthesized from the original set of features. Feature subset selection and feature
… or generated) features should be optimized to the text categorization goal. Intuitively, the …

… effect of the distributional features. The question that we attempt to answer is: are … distributional
features useful for text categorization? For eight features (TF+3 CP features+4 FA features…

… as WiseWire [21], can involve text categorization with hundreds of thousands of class labels
on a … We believe Distributional Clustering performs better than feature selection because …

… text categorization methods is not effective. In this work we propose using distributional term
representations (DTRs) for short-text … performance of classifiers in short-text categorization. …

… text processing techniques. The rest of the paper is organized as follows: in Section 2 we
describe the problem of text categorization; … to the distributional clustering of features (unigrams …