The acquisition of syntactic knowledge
The acquisition of syntactic knowledge
Learning automata: an introduction
Learning automata: an introduction
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
Statistical Language Learning
Unsupervised language acquisition
Unsupervised language acquisition
Automatic grammar induction and parsing free text: a transformation-based approach
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Modeling the effect of cross-language ambiguity on human syntax acquisition
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Bayesian learning over conflicting data: predictions for language change
SigMorPhon '08 Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology
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This paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a formal theory that meets this criterion. We first present developmental facts that are problematic for statistical learning approaches which assume no prior knowledge of grammar, and for traditional learnability models which assume the learner moves from one UG-defined grammar to another. In contrast, we view language acquisition as a population of grammars associated with "weights", that compete in a Darwinian selectionist process. Selection is made possible by the variational properties of individual grammars; specifically, their differential compatibility with the primary linguistic data in the environment. In addition to a convergence proof, we present empirical evidence in child language development, that a learner is best modeled as multiple grammars in co-existence and competition.