@inproceedings{c45269be3d0e45959c18425e401f9447,
title = "A Bayesian Framework for Learning Words From Multiword Utterances",
abstract = "Current computational models of word learning make use of correspondences between words and observed referents, but as of yet cannot-as human learners do-leverage information regarding the meaning of other words in the lexicon. Here we develop a Bayesian framework for word learning that learns a lexicon from multiword utterances. In a set of three simulations we demonstrate this framework's functionality, consistency with experimental work, and superior performance in certain learning tasks with respect to a Bayesian word leaning model that treats word learning as inferring the meaning of each word independently. This framework represents the first step in modeling the potential synergies between referential and distributional cues in word learning.",
keywords = "Bayesian inference, artificial language learning, distributional learning, word learning",
author = "Meylan, {Stephan C.} and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} Cognitive Science Society, CogSci 2015.All rights reserved.; 37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 ; Conference date: 23-07-2015 Through 25-07-2015",
year = "2015",
language = "English (US)",
series = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
publisher = "The Cognitive Science Society",
pages = "1583--1588",
editor = "Noelle, {David C.} and Rick Dale and Anne Warlaumont and Jeff Yoshimi and Teenie Matlock and Jennings, {Carolyn D.} and Maglio, {Paul P.}",
booktitle = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
}