Supervised Term Weighting for Automated Text Categorization

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 tfidf weighting, obtained by replacing the idf function with...

• Type: Conference
• Year: 2003
• Where: SAC
• Authors: Franca Debole, Fabrizio Sebastiani
• Keywords: Applied Computing, SAC 2003, Term Selection, Term Weighting, Training Documents