Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper we criticize existing computational models of lexicon for assuming that for every word there is a fixed number of word sense that must be searched for the proper meaning of that word in a context. We reject this sense enumerative view and argue for a different model of lexicon in which the effects of context are not limited to selecting a word sense, and selected senses can be contextually modulated. W e also explain how patterns of contextual effects could evolve in an exemplar-based fashion. A prototype implementation of this model is also discussed.