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Trends Cogn Sci. 2006 Jul;10(7):335-44. Epub 2006 Jun 19.

Probabilistic models of language processing and acquisition.

Author information

1
Department of Psychology, University College London, Gower Street, London, WC1E 6BT, UK. n.chater@ucl.ac.uk

Abstract

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.

PMID:
16784883
DOI:
10.1016/j.tics.2006.05.006
[Indexed for MEDLINE]

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