Specificity Helps Text Classification

Lucas Bouma, Maarten de Rijke

Abstract: We examine the impact on classification effectiveness of semantic differences in categories. Specifically, we measure broadness and narrowness of categories in terms of their distance to the root of a hierarchically organized thesaurus. Using categories of four different levels degrees of broadness, we show that classifying documents into narrow categories gives better scores than classifying them into broad terms, which we attribute to the fact that more specific categories are associated with terms with a higher discriminatory power.