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Probabilistic models of language processing and acquisition

Chater, Nick, Manning, Christopher D.·2006·Trends in Cognitive Sciences

DOI 10.1016/j.tics.2006.05.006

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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.

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Authors: Chater, N., & Manning, C. D.
Affiliations: Stanford University (US)
Corresponding author: Manning, Christopher D.
ORCID: Manning, Christopher D.
Venue: Trends in Cognitive Sciences
Publisher: Elsevier BV
Published: 2006-07
Volume: 10
Issue: 7
Pages: 335-344
ISSN: 1364-6613 (ISSN-L)
DOI: 10.1016/j.tics.2006.05.006
OpenAlex: W2162693531
Semantic Scholar: c351bcc20d0da75f23daaf20846cad0b93c4f36b
Cited by: 411
References: 90
Citations by year: 2026: 12 · 2025: 9 · 2024: 36 · 2023: 10 · 2022: 12 · 2021: 9 · 2020: 35 · 2019: 53 · 2018: 14 · 2017: 9 · 2016: 21 · 2015: 30 · 2014: 19 · 2013: 18 · 2012: 19
FWCI: 4.10
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Trends in Cognitive Sciences (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: Economic and Social Research Council (RES-538-28-1001)
Topics: Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
Keywords: probabilistic models, language acquisition, language comprehension, language production, probabilistic inference, symbolic models, poverty of the stimulus, ambiguity resolution, categorization, online corpora
MeSH terms: Brain, Cognition, Concept Formation, Humans, Language Development, Phonetics, Probability Theory, Psycholinguistics, Reading, Semantics, Speech Perception, Models, Statistical, Comprehension, Uncertainty

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