Evaluating semantic metrics on tasks of concept similarity

In this study, first, concept similarity measures are evaluated over human judgments by using existing sets of word similarity pairs that we annotated with word senses. Next, an application-oriented study is presented to evaluate semantic metrics based on integration into an algorithm, first focused on the task of concept similarity then on the task of concept relatedness. The results found no single measure to be most significantly correlated with human-judgments, while an information content-based measure clearly lead to the best results in the application-oriented task of concept similarity. Reinforcing the difference between tasks of concept similarity and concept relatedness, the best measure for an application-oriented task of concept relatedness was a gloss-based relatedness measure rather than a similarity measure. A major conclusion of this work is that similarity measures may perform differently if embedded in specific applications than if they are compared with human judgments.