A semantic term weighting scheme for text categorization

Traditional term weighting schemes in text categorization, such as TF-IDF, only exploit the statistical information of terms in documents. Instead, in this paper, we propose a novel term weighting scheme by exploiting the semantics of categories and indexing terms. Specifically, the semantics of categories are represented by senses of terms appearing in the category labels as well as the interpretation of them by WordNet. Also, the weight of a term is correlated to its semantic similarity with a category. Experimental results on three commonly used data sets show that the proposed approach outperforms TF-IDF in the cases that the amount of training data is small or the content of documents is focused on well-defined categories. In addition, the proposed approach compares favorably with two previous studies.

► We propose a novel term weighting scheme for text categorization. ► We employ WordNet to interpret and represent the semantics of categories. ► The weight of a term is correlated to its semantic similarity with a category. ► The proposed approach compares favorably with TF-IDF and two related studies.