An extended supervised term weighting method for text categorization

When Support Vector Machines (SVM) are exploited for automatic text categorization, text representation and term weighting have a significant impact on the performance of text classification. Conventional supervised weighting methods only focus on the frequency characteristics of feature terms, without consideration of semantic characteristics of them. Inspired by supervised weighting method, semantic distance between terms and categories is introduced into term weights calculation. The first step is modeling each category with two vectors of feature terms, which are called category core terms, and acquiring these terms by machine learning methods. Second, the semantic distance between feature terms and category core terms is calculated based on semantic database. Third, the global weight factor is replaced by the sematic distance to calculate the weight of every term. Based on the standard benchmark Reuters-21578, this kind of term weighting schemas can generally produce satisfied results of classification using SVMlight as classifier with default parameters. © 2011 Springer Science+Business Media B.V.