An intelligent thesaurus assists a writer with alternative choices of words and orders them by their suitability in the writing context. In this talk we focus on methods for automatically choosing near-synonyms 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 anti-collocations collected from the Web, and one that uses supervised learning. In order to asses the difficulty of the task, we present results of human judges on this task.


BIO:

Dr. Diana Inkpen is a professor of Computer Science at the School of Information Technology and Engineering, University of Ottawa since July 2003. She obtained her doctorate from the University of Toronto, Department of Computer Science. She has a Masters in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her research projects and publications are in the areas of Computational Linguistics and Artificial Intelligence, more specifically: Information Retrieval, Information Extraction, Natural Language Understanding, Natural Language Generation, Speech Processing, and Intelligent Agents for the Semantic Web.