@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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