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Abstract

The computational approach to syntactic acquisition can be fruitfully pursued by integrating results and perspectives from computer science, linguistics, and developmental psychology. In this article, we first review some key results in computational learning theory and their implications for language acquisition. We then turn to examine specific learning models, some of which exploit distributional information in the input while others rely on a constrained space of hypotheses, yet both approaches share a common set of characteristics to overcome the learning problem. We conclude with a discussion of how computational models connects with the empirical study of child grammar, making the case for computationally tractable, psychologically plausible and developmentally realistic models of acquisition. WIREs Cogn Sci 2012, 3:205–213. doi: 10.1002/wcs.1154

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