Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Proceedingsofthe27thAnnualConferenceoftheCognitiveScienceSociety(CogSci2005) A Probabilistic Model of Early Argument Structure Acquisition
 

Summary: Proceedingsofthe27thAnnualConferenceoftheCognitiveScienceSociety(CogSci2005)
A Probabilistic Model of Early Argument Structure Acquisition
Afra Alishahi and Suzanne Stevenson
Department of Computer Science
University of Toronto
{afra,suzanne}@cs.toronto.edu
Abstract
We present a computational model of usage-based learn-
ing of verb argument structure in young children. The
model integrates Bayesian classification and prediction
to learn from utterances paired with appropriate seman-
tic representations. The model balances item-based and
class-based knowledge in language use, demonstrating
appropriate word order generalizations, and recovery
from overgeneralizations with no negative evidence or
change in learning parameters.
Argument Structure Acquisition
Verb argument structure is a complex aspect of language
for a child to master, as it requires learning the rela-
tions of arguments to a verb, and how those arguments

  

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

 

Collections: Computer Technologies and Information Sciences