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Bootstrapping language acquisition.

Omri Abend1, Tom Kwiatkowski1, Nathaniel J Smith1

  • 1Informatics, University of Edinburgh, United Kingdom.

Cognition
|April 17, 2017
PubMed
Summary
This summary is machine-generated.

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Children learn language by connecting sentence meanings to words and grammar. This Bayesian model explains vocabulary spurts and word learning through statistical analysis of language input.

Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Computational Linguistics

Background:

  • The semantic bootstrapping hypothesis suggests language acquisition relies on pairing sentences with meaning representations.
  • Children learn language by mapping words and syntactic structures to conceptual components.

Purpose of the Study:

  • To develop a Bayesian probabilistic model for semantically bootstrapped first-language acquisition.
  • To investigate how statistical learning over structured representations explains key developmental phenomena.

Main Methods:

  • A Bayesian probabilistic computational model integrating word and syntax learning.
  • Utilizing an incremental learning algorithm applied to child-directed utterances and logical forms.
  • Employing techniques from computational parsing and interpretation of unrestricted text.
Keywords:
Bayesian modelComputational modelingCross-situational learningLanguage acquisitionSemantic bootstrappingSyntactic bootstrapping

Related Experiment Videos

Main Results:

  • The model simulates syntactic bootstrapping, vocabulary spurts, and noun-verb biases.
  • Demonstrates sudden learning jumps and one-shot word learning.
  • Successfully models phenomena observed in the CHILDES corpus (Eve section).

Conclusions:

  • Statistical learning over structured representations offers a unified account of first-language acquisition.
  • The model provides insights into how children acquire both lexicon and grammar.
  • Computational approaches can elucidate complex developmental processes in language acquisition.