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The probabilistic analysis of language acquisition : theoretical, computational, and experimental analysis

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Hsu, Anne S., Chater, Nick and Vitányi, P. M. B. (2011) The probabilistic analysis of language acquisition : theoretical, computational, and experimental analysis. Cognition, Vol.120 (No.3). pp. 380-390. doi:10.1016/j.cognition.2011.02.013 ISSN 0010-0277.

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Official URL: http://dx.doi.org/10.1016/j.cognition.2011.02.013

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Abstract

There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

Item Type: Journal Article
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Language acquisition -- Mathematical models, Language acquisition -- Statistical methods, Bayesian statistical decision theory, Natural language processing (Computer science)
Journal or Publication Title: Cognition
Publisher: Elsevier BV
ISSN: 0010-0277
Official Date: September 2011
Dates:
DateEvent
September 2011Published
Volume: Vol.120
Number: No.3
Page Range: pp. 380-390
DOI: 10.1016/j.cognition.2011.02.013
Status: Peer Reviewed
Publication Status: Published

Data sourced from Thomson Reuters' Web of Knowledge

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