Supervised term weighting for automated text categorization

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Abstract
The construction of a text classifier usually involves (i) a phase of term selection, in which the most relevant terms for the classification task are identified, (ii) a phase of term weighting, in which document weights for the selected terms are computed, and (iii) a phase of classifier learning, in which a classifier is generated from the weighted representations of the training documents. This process involves an activity of supervised learning, in which information on the membership of training documents in categories is used. Traditionally, supervised learning enters only phases (i) and (iii). In this paper we propose instead that learning from training data should also affect phase (ii), i.e. that information on the membership of training documents to categories be used to determine term weights. We call this idea supervised term weighting (STW). As an example, we propose a number of "supervised variants" of t f idf weighting, obtained by replacing the idf function with the function that has been used in phase (i) for term selection. We present experimental results obtained on the standard Reuters-21578 benchmark with one classifier learning method (support vector machines), three term selection functions (information gain, chi-square, and gain ratio), and both local and global term selection and weighting.