Evaluation of Scalable Database Driven Reverse Dictionary

Abstract

While a traditional forward dictionary maps words to their definitions, a reverse dictionary takes a user input phrase with a desired concept, and returns a set of candidate words closely related to the input phrase. This application is significant not only for the general public, but mainly to those who work personally with words. It is also important in the general field of conceptual search. Upon receiving a search concept, the Reverse Dictionary consults the forward dictionary and selects those words whose definitions are similar to the given concept. And thus it is reduced to a concept similarity problem. In this paper, different concept similarity measures are compared and the best among them is proposed. The experimental results shows that the approach used here provides significant improvements in performance level without losing the quality of the result.