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{ "pk": 25645, "title": "A Bayesian Framework for LearningWords From Multiword Utterances", "subtitle": null, "abstract": "Current computational models of word learning make use of\ncorrespondences between words and observed referents, but as\nof yet cannot—as human learners do—leverage information\nregarding the meaning of other words in the lexicon. Here we\ndevelop a Bayesian framework for word learning that learns\na lexicon from multiword utterances. In a set of three simulations\nwe demonstrate this framework’s functionality, consistency\nwith experimental work, and superior performance in\ncertain learning tasks with respect to a Bayesian word leaning\nmodel that treats word learning as inferring the meaning of\neach word independently. This framework represents the first\nstep in modeling the potential synergies between referential\nand distributional cues in word learning", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "word learning; Bayesian inference; artificial language\nlearning; distributional learning" } ], "section": "Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/0wk7n803", "frozenauthors": [ { "first_name": "Stephan", "middle_name": "C", "last_name": "Meylan", "name_suffix": "", "institution": "University of California, Berkeley", "department": "" }, { "first_name": "Thomas", "middle_name": "L", "last_name": "Griffiths", "name_suffix": "", "institution": "University of California, Berkeley", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2015-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/25645/galley/15269/download/" } ] }