Near-Synonym Choice in an Intelligent Thesaurus

An intelligent thesaurus assists a writer with alternative choices of words and orders them by their suitability in the writing context. In this paper we focus on methods for automatically choosing nearsynonyms by their semantic coherence with the context. Our statistical method uses the Web as a corpus to compute mutual information scores. Evaluation experiments show that this method performs better than a previous method on the same task. We also propose and evaluate two more methods, one that uses anticollocations, and one that uses supervised learning. To asses the difficulty of the task, we present results obtained by human judges.