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A Probabilistic Computational Model of Cross-Situational Word Learning
 

Summary: A Probabilistic Computational Model of Cross-Situational
Word Learning
Afsaneh Fazly,a
Afra Alishahi,b
Suzanne Stevensona
a
Department of Computer Science, University of Toronto
b
Department of Computational Linguistics, Saarland University
Received 14 November 2008; received in revised form 22 December 2009; accepted 04 January 2010
Abstract
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 acqui-
sition: 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 among the different theories
is whether children are equipped with special mechanisms and biases for word learning, or
their general cognitive abilities are adequate for the task. We present a novel computational
model of early word learning to shed light on the mechanisms that might be at work in this

  

Source: Alishahi, Afra - Department of Computational Linguistics and Phonetics, Universitšt des Saarlandes

 

Collections: Computer Technologies and Information Sciences