1.  52
    A Probabilistic Computational Model of Cross-Situational Word Learning.Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson - 2010 - Cognitive Science 34 (6):1017-1063.
    Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...)
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    A Computational Model of Early Argument Structure Acquisition.Afra Alishahi & Suzanne Stevenson - 2008 - Cognitive Science 32 (5):789-834.
    How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of (...)
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  3. A probabilistic incremental model of word learning in the presence of referential uncertainty.Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.