PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Integrating Syntactic Knowledge into a Model of Cross-situational Word Learning
Afra Alishahi and Afsaneh Fazly
In: The 32nd Annual Meeting of the Cognitive Science Society, August 11-14, 2010, Portland, Oregon, US.


It has been suggested that children learn the meanings of words by observing the regularities across different situations in which a word is used. However, experi- mental studies show that children are also sensitive to the syntactic properties of words and their context at a young age, and can use this information to find the correct referent for novel words. We present a unified computational model of word learning which integrates cross-situational evidence with the accumulated seman- tic properties of the lexical categories of words. Our experimental results show that using lexical categories can improve performance in learning, particularly for novel or low-frequency words in ambiguous situations.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7916
Deposited By:Afra Alishahi
Deposited On:17 March 2011