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A Probabilistic Computational Model of Cross-situational Word Afsaneh Fazly, Afra Alishahi, Suzanne Stevenson
 

Summary: A Probabilistic Computational Model of Cross-situational Word
Learning
Afsaneh Fazly, Afra Alishahi, Suzanne Stevenson
Department of Computer Science
University of Toronto
Canada
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 theo-
ries 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
process. The model learns word meanings as probabilistic associations between words and se-
mantic elements, using an incremental and probabilistic learning mechanism, and drawing only
on general cognitive abilities. The results presented here demonstrate that much about word
meanings can be learned from naturally-occurring child-directed utterances (paired with mean-

  

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

 

Collections: Computer Technologies and Information Sciences