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Bootstrapping language acquisition.
Omri Abend1, Tom Kwiatkowski1, Nathaniel J Smith1
1Informatics, University of Edinburgh, United Kingdom.
Cognition
|April 17, 2017
Summary
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.
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.