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UID:cb5b61c82870613a0d3a3ddc5b2999cb CATEGORIES:RuCCS Colloquia:Fall 2014 CREATED:20151203T141544 SUMMARY:Learning the meaning of words: A probabilistic computational model LOCATION:University of Toronto\, Department of Computer Science DESCRIPTION:An average five-year-old knows 10,000-15,000 words, most of which she’s hea rd only in ambiguous contexts – that is, when she hears an utterance, the c hild must determine which of numerous possible concepts is being talked abo ut, and must further figure out which word goes along with which of those m eanings. The open-ended nature of the input to children has often been use d as an argument for the necessity of innate, language-specific mechanisms that enable them to focus their learning appropriately. More recently, how ever, a number of researchers have instead claimed that general cognitive a bilities should be sufficient to the task of word learning. We’ve develope d a computational model that helps to shed light on this debate by demonstr ating that word–meaning mappings can be acquired through a general probabil istic learning mechanism. The model incrementally builds up (probabilistic ) associations between words and meanings when exposed to naturalistic data of words in context, without the use of special biases or constraints. In this talk, I’ll describe the model along with some of its behaviours that mimic aspects of child word learning, such as fast mapping and the spacing effect.\n\n This is joint work with Afsaneh Fazly, Afra Alishahi, and Aida Nematzadeh.\n X-ALT-DESC;FMTTYPE=text/html:
An average five-year-old knows 10,000-15,000 words, most of which she&rs quo;s heard only in ambiguous contexts – that is, when she hears an u tterance, the child must determine which of numerous possible concepts is b eing talked about, and must further figure out which word goes along with w hich of those meanings. The open-ended nature of the input to childre n has often been used as an argument for the necessity of innate, language- specific mechanisms that enable them to focus their learning appropriately. More recently, however, a number of researchers have instead claimed that general cognitive abilities should be sufficient to the task of word learning. We’ve developed a computational model that helps to s hed light on this debate by demonstrating that word–meaning mappings can be acquired through a general probabilistic learning mechanism. T he model incrementally builds up (probabilistic) associations between words and meanings when exposed to naturalistic data of words in context, withou t the use of special biases or constraints. In this talk, I’ll describe the model along with some of its behaviours that mimic aspects of child word learning, such as fast mapping and the spacing effect.
This is joint work with Afsaneh Fazly, Afra Alishahi, and Aida Nematzad
eh.