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An empirical generative framework for computational modeling of language acquisition*

Published online by Cambridge University Press:  26 April 2010

Heidi R. Waterfall
Affiliation:
Department of Psychology, Cornell University and Department of Psychology, University of Chicago
Ben Sandbank
Affiliation:
School of Computer Science, Tel Aviv University
Luca Onnis
Affiliation:
Department of Second Language Studies, University of Hawaii
Shimon Edelman
Affiliation:
Department of Psychology, Cornell University and Department of Brain and Cognitive Engineering, Korea University

Abstract

This paper reports progress in developing a computer model of language acquisition in the form of (1) a generative grammar that is (2) algorithmically learnable from realistic corpus data, (3) viable in its large-scale quantitative performance and (4) psychologically real. First, we describe new algorithmic methods for unsupervised learning of generative grammars from raw CHILDES data and give an account of the generative performance of the acquired grammars. Next, we summarize findings from recent longitudinal and experimental work that suggests how certain statistically prominent structural properties of child-directed speech may facilitate language acquisition. We then present a series of new analyses of CHILDES data indicating that the desired properties are indeed present in realistic child-directed speech corpora. Finally, we suggest how our computational results, behavioral findings, and corpus-based insights can be integrated into a next-generation model aimed at meeting the four requirements of our modeling framework.

Type
Articles
Copyright
Copyright © Cambridge University Press 2010

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