A Bayesian Framework for Learning Words From Multiword Utterances

Stephan C. Meylan, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
EditorsDavid C. Noelle, Rick Dale, Anne Warlaumont, Jeff Yoshimi, Teenie Matlock, Carolyn D. Jennings, Paul P. Maglio
PublisherThe Cognitive Science Society
Pages1583-1588
Number of pages6
ISBN (Electronic)9780991196722
StatePublished - 2015
Externally publishedYes
Event37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 - Pasadena, United States
Duration: Jul 23 2015Jul 25 2015

Publication series

NameProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015

Conference

Conference37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015
Country/TerritoryUnited States
CityPasadena
Period7/23/157/25/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • artificial language learning
  • Bayesian inference
  • distributional learning
  • word learning

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