Probabilistic models of language processing and acquisition. Chater, N. & Manning, C. D Trends Cogn Sci, 10(7):335-44, 2006. doi abstract bibtex 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.
@Article{Chater2006,
author = {Nick Chater and Christopher D Manning},
journal = {Trends Cogn Sci},
title = {Probabilistic models of language processing and acquisition.},
year = {2006},
number = {7},
pages = {335-44},
volume = {10},
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.},
doi = {10.1016/j.tics.2006.05.006},
keywords = {Brain, Cognition, Comprehension, Concept Formation, Humans, Language Development, Models, Phonetics, Probability Theory, Psycholinguistics, Reading, Semantics, Speech Perception, Statistical, Uncertainty, 16784883},
}
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