Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 285

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

The learnability of abstract syntactic principles

Perfors, Amy, Tenenbaum, Joshua B., Regier, Terry·2011·Cognition

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cognition.2010.11.001

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 55

  1. Alishahi, A., & Stevenson, S. (2005). A probabilistic model of early argument structure acquisition. In Proceedings of the 27th annual conference of the cognitive science society.
  2. Ambridge, 2008, Is structure dependence an innate constraint? New experimental evidence from children’s complex-question production, Cognitive Science, 32, 222, 10.1080/03640210701703766 · DOI
  3. Anderson, 1991, The adaptive nature of human categorization, Psychology Review, 98, 409, 10.1037/0033-295X.98.3.409 · DOI · Paper page
  4. Berwick, 1986, Learning from positive-only examples: The subset principle and three case studies, Machine Learning, 2, 625
  5. Berwick, R., & Chomsky, N. (2008). ‘Poverty of the stimulus’ revisited: Recent challenges reconsidered. In Proceedings of the 30th annual conference of the cognitive science society.
  6. Booth, 2003, Mapping words to the world in infancy: Infants’ expectations for count nouns and adjectives, Journal of Cognition and Development, 4, 357, 10.1207/S15327647JCD0403_06 · DOI
  7. Borensztajn, G., Zuidema, W., & Bod, R. (2008). Children’s grammars grow more abstract with age - evidence from an automatic procedure for identifying the productive units of language. In Proceedings of the 30th annual conference of the cognitive science society.
  8. Borovsky, 2006, Language input and semantic categories: A relation between cognition and early word learning, Journal of Child Language, 33, 759, 10.1017/S0305000906007574 · DOI
  9. Briscoe, E. (2006). Language learning, power laws, and sexual selection. In 6th international conference on the evolution of language. · DOI
  10. Chater, 1999, Ten years of the rational analysis of cognition, Trends in Cognitive Sciences, 3, 57, 10.1016/S1364-6613(98)01273-X · DOI
  11. Chater, 2006, Probabilistic models of cognition: Conceptual foundations, Trends in Cognitive Sciences, 10, 292, 10.1016/j.tics.2006.05.008 · DOI
  12. Chater, 2007, ‘Ideal learning’ of natural language: Positive results about learning from positive evidence, Journal of Mathematical Psychology, 51, 135, 10.1016/j.jmp.2006.10.002 · DOI
  13. Chomsky, 1956, Three models for the description of language, IRE Transactions on Information Theory, 2, 113, 10.1109/TIT.1956.1056813 · DOI
  14. Clark, A., & Eyraud, R. (2006). Learning auxiliary fronting with grammatical inference. In Proceedings of the 28th annual conference of the cognitive science society. · DOI
  15. Collins, M. (1999). Head-driven statistical models for natural language parsing. Unpublished doctoral dissertation, University of Pennsylvania.
  16. Crain, 1991, Language acquisition in the absence of experience, Behavioral and Brain Sciences, 14, 597, 10.1017/S0140525X00071491 · DOI
  17. Dowman, M. (2000). Addressing the learnability of verb subcategorizations with Bayesian inference. In Proceedings of the 22nd annual conference of the cognitive science society.
  18. Edelsbrunner, 2000, Edgewise subdivision of a simplex, Discrete Computational Geometry, 24, 707, 10.1007/s4540010063 · DOI
  19. Eisner, 2002, Discovering deep structure via Bayesian statistics, Cognitive Science, 26, 255, 10.1207/s15516709cog2603_3 · DOI
  20. Everett, 2005, Cultural constraints on grammar and cognition in Pirahã: Another look at the design features of human language, Current Anthropology, 46, 621, 10.1086/431525 · DOI · Paper page
  21. Feldman, J., Gips, J., Horning, J., & Reder, S. (1969) Grammatical complexity and inference (tech. rep. CS-TR-69-125). Stanford Univ. · DOI
  22. Foraker, 2009, Indirect evidence and the poverty of the stimulus: The case of anaphoric one, Cognitive Science, 33, 287, 10.1111/j.1551-6709.2009.01014.x · DOI
  23. Gerken, 2005, Infants can use distributional cues to form syntactic categories, Journal of Child Language, 32, 249, 10.1017/S0305000904006786 · DOI
  24. Gold, 1967, Language identification in the limit, Information and Control, 10, 447, 10.1016/S0019-9958(67)91165-5 · DOI · Paper page
  25. Goldwater, S., & Griffiths, T. (2007). A fully Bayesian approach to unsupervised part-of speech tagging. In 45th annual meeting of the association for computational linguistics.
  26. Goldwater, 2006, Interpolating between types and tokens by estimating power law generators, Neural Information Processing Systems, 18
  27. Griffiths, T., Baraff, E., & Tenenbuam, J. (2004). Using physical theories to infer hidden causal structure. In proceedings of the 26th annual conference of the cognitive science society.
  28. Griffiths, 2005, Integrating topics and syntax, Neural Information Processing Systems, 17, 9
  29. Ishwaran, 2003, Generalized weighted Chinese restaurant processes for species sampling mixture models, Statistica Sinica, 13, 1211
  30. Johnson, 2007, Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models, Neural Information Processing Systems, 19
  31. Johnson, 2002, Statistical models of syntax learning and use, Cognitive Science, 239, 10.1207/s15516709cog2603_2 · DOI
  32. Kam, 2008, Bigrams and the richness of the stimulus, Cognitive Science, 32, 771, 10.1080/03640210802067053 · DOI
  33. Kearns, M., & Valiant, L. (1989). Cryptographic limitations on learning (Boolean) formulae and finite automata. In Proceedings of the 21st annual ACM symposium on theory of computing, pp. 433–444.
  34. Kemp, 2008, The discovery of structural form, Proceedings of the National Academy of Sciences, 105, 10687, 10.1073/pnas.0802631105 · DOI
  35. Klein, D., & Manning, C. (2004). Corpus-based induction of syntactic structure: Models of dependency and constituency. In Proceedings of the 42nd annual meeting of the association for computational linguistics, pp. 479–486. · DOI · Paper page
  36. Landauer, 1997, A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge, Psychological Review, 104, 211, 10.1037/0033-295X.104.2.211 · DOI · Paper page
  37. Laurence, 2001, The poverty of the stimulus argument, British Journal of the Philosophy of Science, 52, 217, 10.1093/bjps/52.2.217 · DOI
  38. Legate, 2002, Empirical re-assessment of stimulus poverty arguments, Linguistic Review, 19, 151, 10.1515/tlir.19.1-2.151 · DOI
  39. Lewis, J., & Elman, J. (2001). Learnability and the statistical structure of language: Poverty of stimulus arguments revisited. In Proceedings of the 26th Boston University conference on language development.
  40. Liang, P., Petrov, S., Jordan, M., & Klein, D. (2007). The infinite PCFG using hierarchical Dirichlet processes. In Proceedings of EMNLP.
  41. Light, 2002, Statistical models for the induction and use of selectional preferences, Cognitive Science, 26, 269, 10.1207/s15516709cog2603_4 · DOI
  42. Mintz, 2002, The distributional structure of grammatical categories in speech to young children, Cognitive Science, 26, 393, 10.1207/s15516709cog2604_1 · DOI
  43. Pinker, 2005, The faculty of language: What’s special about it?, Cognition, 95, 201, 10.1016/j.cognition.2004.08.004 · DOI · Paper page
  44. Pitman, 1997, The two-parameter Poisson–Dirichlet distribution derived from a stable subordinator, Annals of Probability, 25, 855, 10.1214/aop/1024404422 · DOI · Paper page
  45. Pullum, 2002, Empirical assessment of stimulus poverty arguments, Linguistic Review, 19, 9, 10.1515/tlir.19.1-2.9 · DOI
  46. Reali, F., & Christiansen, M. (2004). Structure dependence in language acquisition: Uncovering the statistical richness of the stimulus. In Proceedings of the 26th annual conference of the cognitive science society.
  47. Regier, 2004, Learning the unlearnable: The role of missing evidence, Cognition, 93, 147, 10.1016/j.cognition.2003.12.003 · DOI
  48. Rose Finkel, J., Grenager, T., & Manning, C. (2007). The infinite tree. In Proceedings of ACL.
  49. Rumelhart, 1986, Vol. 1
  50. Sagae, K., Davis, E., Lavie, A., MacWhinney, B., & Wintner, S. (2007). High-accuracy annotation and parsing of CHILDES transcripts. In Proceedings of ACL-2007 workshop on cognitive aspects of computational language acquisition. · DOI
  51. Schütze, H. (1995). Distributional part-of-speech tagging. In Proceedings of the 7th conference of the European chapter of the association for computational linguistics. · DOI
  52. Solomonoff, 1978, Complexity-based induction systems: Comparisons and convergence theorems, IEEE Transactions on Information Theory, 24, 422, 10.1109/TIT.1978.1055913 · DOI · Paper page
  53. Tenenbaum, 2001, Generalization, similarity, and Bayesian inference, Behavioral and Brain Sciences, 24, 629, 10.1017/S0140525X01000061 · DOI · Paper page
  54. Tomasello, 2000, The item based nature of children’s early syntactic development, Trends in Cognitive Sciences, 4, 156, 10.1016/S1364-6613(00)01462-5 · DOI · Paper page
  55. Xu, 2007, Word learning as bayesian inference, Psychological Review, 114, 245, 10.1037/0033-295X.114.2.245 · DOI · Paper page

Cited by

See all 285

Metadata

Authors
Perfors, A., Tenenbaum, J. B., & Regier, T.
Venue
Cognition
Publisher
Elsevier BV
Published
2011-03
Volume
118
Issue
3
Pages
306-338
ISSN
0010-0277 (ISSN-L)
OpenAlex
W2154346509
Cited by
285
References
10
FWCI
33.64
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.