A New Context-Aware Measure for Semantic Distance Using a Taxonomy and a Text Corpus

Having a reliable semantic similarity measure between words/concepts can have major effect in many fields like information retrieval and information integration. A major lack in the existing semantic similarity measures is that no one takes into account the actual context or the considered domain. However, two concepts similar in one context may appear completely unrelated in another context. In this paper, we present a new context-based semantic distance. Then, we propose to combine it with classical approaches dealing with taxonomies and corpora. Our correlation ratio of 0.89 with human judgments on a set of words pairs led our approach to outperform all the other approaches.