The DOI matches its Crossref record; 87 of the 90 references it cites were found in a registry or an index.
Probabilistic models of language processing and acquisition
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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“Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [6]
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“Language interpretation is a high-level cognitive task which shows a good correspondence to Bayesian models [5,6].”
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Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Self-citationLectures on Government and Binding
Chomsky, N. (1993) · Mouton eBooks · doi:10.1515/9783110884166 · Cited by 8,587
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Williams, E. (1984) · Language · doi:10.2307/413646 · Cited by 5,173
Constructing a language: a usage-based theory of language acquisition
(2003) · Choice Reviews Online · doi:10.5860/choice.41-1385 · Cited by 4,455
Language identification in the limit
Gold, E. M. (1967) · Information and Control · doi:10.1016/s0019-9958(67)91165-5 · Cited by 3,572
The TRACE model of speech perception
McClelland, J. L., & Elman, J. L. (1986) · Cognitive Psychology · doi:10.1016/0010-0285(86)90015-0 · Cited by 3,044
The motor theory of speech perception revised
Liberman, A. M., & Mattingly, I. G. (1985) · Cognition · doi:10.1016/0010-0277(85)90021-6 · Cited by 2,970
Integration of Visual and Linguistic Information in Spoken Language Comprehension
Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995) · Science · doi:10.1126/science.7777863 · Cited by 2,929
Prediction and Entropy of Printed English
Shannon, C. E. (1951) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1951.tb01366.x · Cited by 2,686
Toward a mechanistic psychology of dialogue.
Pickering, M. J., & Garrod, S. (2004) · Behavioral and Brain Sciences · doi:10.1017/s0140525x04000056 · Cited by 2,678
Head-Driven Statistical Models for Natural Language Parsing
Collins, M. (2003) · Computational Linguistics · doi:10.1162/089120103322753356 · Cited by 1,822
The sausage machine: A new two-stage parsing model
Frazier, L., & Fodor, J. D. (1978) · Cognition · doi:10.1016/0010-0277(78)90002-1 · Cited by 1,348
Rule Learning by Seven-Month-Old Infants
Marcus, G., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999) · Science · doi:10.1126/science.283.5398.77 · Cited by 1,246
Words and rules: the ingredients of language
(2000) · Choice Reviews Online · doi:10.5860/choice.37-5964 · Cited by 1,230
Empirical Tests of the Gradual Learning Algorithm
Boersma, P., & Hayes, B. (2001) · Linguistic Inquiry · doi:10.1162/002438901554586 · Cited by 1,121
Empirical assessment of stimulus poverty arguments
Pullum, G. K., & Scholz, B. C. (2002) · The Linguistic Review · doi:10.1515/tlir.19.1-2.9 · Cited by 923
Bayesian decision theory in sensorimotor control
Kording, K. P., & Wolpert, D. M. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.003 · Cited by 916
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880 · cited 2×
“The first issue requires a probabilistic model of general knowledge ([23] and Tenenbaum et al., this issue [ 24 ]).”
Vision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849
Learning at a distance I. Statistical learning of non-adjacent dependencies
Newport, E. L., & Aslin, R. Ν. (2003) · Cognitive Psychology · doi:10.1016/s0010-0285(03)00128-2 · Cited by 835
Zettlemoyer, L., & Collins, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1420 · Cited by 815
Clair, R. S., Fodor, J. A., Bever, T. G., & Garrett, M. F. (1974) · Modern Language Journal · doi:10.2307/325153 · Cited by 793
Unsupervised Learning of the Morphology of a Natural Language
Goldsmith, J. (2001) · Computational Linguistics · doi:10.1162/089120101750300490 · Cited by 790
Rules vs. analogy in English past tenses: a computational/experimental study
Albright, A., & Hayes, B. (2003) · Cognition · doi:10.1016/s0010-0277(03)00146-x · Cited by 777
Merging information in speech recognition: Feedback is never necessary
Norris, D. G., McQueen, J. M., & Cutler, A. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00003241 · Cited by 771
A Probabilistic Model of Lexical and Syntactic Access and Disambiguation
Jurafsky, D. S. (1996) · Cognitive Science · doi:10.1207/s15516709cog2002_1 · Cited by 718
Applications of stochastic context-free grammars using the Inside-Outside algorithm
Lari, K., & Young, S. J. (1991) · Computer Speech & Language · doi:10.1016/0885-2308(91)90009-f · Cited by 639
Pinker, S. A. (1998) · Lingua · doi:10.1016/s0024-3841(98)00035-7 · Cited by 562
Formal models of language learning
Pinker, S. A. (1979) · Cognition · doi:10.1016/0010-0277(79)90001-5 · Cited by 551
Distributional Information: A Powerful Cue for Acquiring Syntactic Categories
Redington, M., Chater, N., & Finch, S. (1998) · Cognitive Science · doi:10.1207/s15516709cog2204_2 · Cited by 535
Statistical parsing with a context-free grammar and word statistics
Eugene Charniak, D. (1997) · Cited by 521
Distributional regularity and phonotactic constraints are useful for segmentation
Brent, M. R., & Cartwright, T. A. (1996) · Cognition · doi:10.1016/s0010-0277(96)00719-6 · Cited by 514 · cited 1×
“Some theorists focus on applying probability to learning parameters of Chomskyan Universal Grammar [54,55]; others focus on learning relatively simple aspects of language, such as syntactic or semantic categories, or approximate morphological decomposition, with relatively weak prior assumptions [44, 56 ,57].”
Sociolinguistics : an international handbook of the science of language and society
Ammon, U., Dittmar, N., & Mattheier, K. J. (1987) · Cited by 510
Griffiths, T. L., Mark Steyvers, B., Blei, D. M., & Tenenbaum, J. B. (2004) · Cited by 501
Consequences of the Serial Nature of Linguistic Input for Sentenial Complexity
Grodner, D. J., & Gibson, E. A. F. (2005) · Cognitive Science · doi:10.1207/s15516709cog0000_7 · Cited by 470
Memory-Based Language Processing
Daelemans, W. M. P., & van den Bosch, A. P. J. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511486579 · Cited by 466
Corpus-based induction of syntactic structure
Klein, D., & Manning, C. D. (2004) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1218955.1219016 · Cited by 451
Self-citationThe Bayesian reader: Explaining word recognition as an optimal Bayesian decision process.
Norris, D. G. (2006) · Psychological Review · doi:10.1037/0033-295x.113.2.327 · Cited by 447
(2003) · The MIT Press eBooks · doi:10.7551/mitpress/5582.001.0001 · Cited by 426
Language, brain, and cognitive development : essays in honor of Jacques Mehler
Emmanuel Dupoux, A. (2001) · Cited by 408
The estimation of stochastic context-free grammars using the Inside-Outside algorithm
Lari, K., & Young, S. J. (1990) · Computer Speech & Language · doi:10.1016/0885-2308(90)90022-x · Cited by 393
Words and Rules: The Ingredients of Language
Algeo, J. T. (2000) · Journal of English Linguistics · doi:10.1177/00754240022005117 · Cited by 389
Östman, J., & Verschueren, J. (1995) · John Benjamins eBooks · Cited by 375
The Computational Nature of Language Learning and Evolution
Niyogi, P. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2024.001.0001 · Cited by 355
Language Acquisition and Use: Learning and Applying Probabilistic Constraints
Seidenberg, M. S. (1997) · Science · doi:10.1126/science.275.5306.1599 · Cited by 338
A Probabilistic Constraints Approach to Language Acquisition and Processing
Seidenberg, M. S., & MacDonald, M. C. (1999) · Cognitive Science · doi:10.1207/s15516709cog2304_8 · Cited by 335
Shifting paradigms: gradient structure in morphology
HAY, J., & Baayen, R. H. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.04.002 · Cited by 334
Eye Movements Reveal the On-Line Computation of Lexical Probabilities During Reading
McDonald, S. A., & Shillcock, R. C. (2003) · Psychological Science · doi:10.1046/j.0956-7976.2003.psci_1480.x · Cited by 322
Selectional constraints: an information-theoretic model and its computational realization
Resnik, P. (1996) · Cognition · doi:10.1016/s0010-0277(96)00722-6 · Cited by 320
The Information Conveyed by Words in Sentences
Hale, J. (2003) · Journal of Psycholinguistic Research · doi:10.1023/a:1022492123056 · Cited by 293
Empirical re-assessment of stimulus poverty arguments
Legate, J. A., & Yang, C. D. (2002) · The Linguistic Review · doi:10.1515/tlir.19.1-2.151 · Cited by 278
Chater, N., & Oaksford, M. (2005) · Wiley eBooks · doi:10.1002/0470018860.s00603 · Cited by 252
Self-citationSemantic density and past-tense formation in three Germanic languages
Baayen, R. H., & Martı́n, F. M. D. P. (2005) · Language · doi:10.1353/lan.2005.0112 · Cited by 250
A generative constituent-context model for improved grammar induction
Klein, D., & Manning, C. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073106 · Cited by 248
Self-citationThe Balancing Act: Combining Symbolic and Statistical Approaches to Language
Klavans, J. L., & Resnik, P. (1996) · Cited by 231
Filling gaps: Decision principles and structure in sentence comprehension
Frazier, L. Q. (1983) · Cognition · doi:10.1016/0010-0277(83)90022-7 · Cited by 219
Ambiguity Resolution in Sentence Processing: Evidence against Frequency-Based Accounts
Pickering, M. J., Traxler, M. J., & Crocker, M. W. (2000) · Journal of Memory and Language · doi:10.1006/jmla.2000.2708 · Cited by 212
Combining distributional and morphological information for part of speech induction
Clark, A. (2003) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1067807.1067817 · Cited by 208
Manning, C. D. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/5582.003.0011 · Cited by 184
Self-citationRationality In An Uncertain World
Chater, N., & Oaksford, M. (2004) · Routledge eBooks · doi:10.4324/9780203345955 · Cited by 174
Self-citationJaeger, D., & Jung, R. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_100494 · Cited by 168
Distributional part-of-speech tagging
Schütze, H. (1995) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/976973.976994 · Cited by 162
German Inflection: Single Route or Dual Route?
Hahn, U., & Nakisa, R. C. (2000) · Cognitive Psychology · doi:10.1006/cogp.2000.0737 · Cited by 150
Do Infants Learn Grammar with Algebra or Statistics?
Seidenberg, M. S., & Elman, J. L. (1999) · Science · doi:10.1126/science.284.5413.433f · Cited by 148
Wide-Coverage Probabilistic Sentence Processing
Crocker, M. W., & Brants, T. (2000) · Journal of Psycholinguistic Research · doi:10.1023/a:1026560822390 · Cited by 125
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
Narayanan, S., & Jurafsky, D. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0012 · Cited by 112
Bunkley Anita, R. (1976) · Cited by 111
Desmet, T., Baecke, C. D., Drieghe, D., Brysbaert, M., & Vonk, W. (2005) · Language and Cognitive Processes · doi:10.1080/01690960400023485 · Cited by 84
Rationality in an Uncertain World
(2005) · Nomos eBooks · doi:10.5771/9783845271224 · Cited by 84
Pragmatics and Computational Linguistics
Jurafsky, D. S. (2006) · Wiley eBooks · doi:10.1002/9780470756959.ch26 · Cited by 71
Gibson, E. A. F. (2006) · Journal of Memory and Language · doi:10.1016/j.jml.2005.12.005 · Cited by 69
Stochastic HPSG Parse Disambiguation using the Redwoods Corpus
Toutanova, K., Manning, C. D., Flickinger, D., & Oepen, S. (2005) · Research on Language and Computation · doi:10.1007/s11168-005-1288-y · Cited by 61
Self-citationDisambiguation preferences and corpus frequencies in noun phrase conjunction
Desmet, T. (2003) · Journal of Memory and Language · doi:10.1016/s0749-596x(03)00025-1 · Cited by 49
NIMH: Caught in the Line of Fire Without a General
Marshall, E. (1995) · Science · doi:10.1126/science.7732368 · Cited by 46
Exploiting a verb lexicon in automatic semantic role labelling
Swier, R., & Stevenson, S. (2005) · doi:10.3115/1220575.1220686 · Cited by 36
(1996) · The MIT Press eBooks · doi:10.7551/mitpress/1507.001.0001 · Cited by 31
Do infants learn grammar with algebra or statistics? [5] (multiple letters)
Seidenberg, M. S., Elman, J. L., Negishi, M., Eimas, P. D., & Marcus, G. (1999) · Science · Cited by 29
Probabilistic Models of Grammar: Phonology as Information Minimization
Goldsmith, J. (2002) · Cited by 28
A Procedure for Grammatical Inference.
Horning, J. J. (1971) · IFIP Congress · Cited by 25
Statistical models of syntax learning and use
Johnson, M. S., & Riezler, S. (2002) · Cognitive Science · doi:10.1207/s15516709cog2603_2 · Cited by 20
Word Learning as Bayesian Inference: Evidence from Preschoolers
Tenenbaums, J. B., & Xu, F. (2005) · eScholarship (California Digital Library) · Cited by 20
Chater, N. (2004) · Journal of Child Language · doi:10.1017/s0305000904006348 · Cited by 11
Self-citation
Flagged first, then by citations
3 of the 90 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Fanselow, G. et al. eds., Gradience in Grammar: Generative Perspectives, Oxford University Press (in press)
- Smolensky, P. and Legendre, G. (2006) The Harmonic Mind (2 Vols), MIT Press
- Gibson, 1994, Triggers. Linguist. Inq., 25, 407
- Pierrehumbert, 2001, Stochastic phonology, Glot Intl., 5, 1
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Sorted by citations
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Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Predictive coding: an account of the mirror neuron system
Kilner, J. M., Friston, K. J., & Frith, C. D. (2007) · Cognitive Processing · doi:10.1007/s10339-007-0170-2 · Cited by 1,134
What do we mean by prediction in language comprehension?
Kuperberg, G. R., & Jaeger, T. F. (2016) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2015.1102299 · Cited by 1,087
Active perception: sensorimotor circuits as a cortical basis for language
Friedemann Pulvermüller, A., & Fadiga, L. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2811 · Cited by 1,105
Metadata
- Authors
- Chater, N., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Corresponding author
- 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)
- 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
- 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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