Grammar Acquisition
Some of my early post-dissertation work explored computational
models of first language acquisition, in particular the acquisition
of syntax. The Amber model, which represented grammatical knowledge
as production rules and relied on a discrimination mechanism for
learning, matched a number of phenomena from the literature on
child language.
My later work in this area was less psychologically oriented
but continued to focus on general learning methods that could be
used in domains other than language. The GRIDS system, which began
as a rational reconstruction of Wolff's SNPR, uses a simplicity
metric to guide search through a space of context-free grammars.
Related Publications
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Langley, P., & Stromsten, S. (2000).
Learning context-free grammars with a simplicity bias.
Proceedings of the Eleventh European Conference on Machine
Learning (pp. 220-228). Barcelona: Springer-Verlag.
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Langley, P. (1991).
Machine learning and language acquisition.
Proceedings of the AAAI Spring Symposium on Machine Learning
of Natural Language and Ontology. Stanford, CA: AAAI Press.
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Langley, P., & Carbonell, J. G. (1987).
Language acquisition and machine learning.
In B. MacWhinney (Ed.), Mechanisms of language acquisition.
Hillsdale, NJ: Lawrence Erlbaum.
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Langley, P. (1987).
Machine learning and grammar induction.
Machine Learning, 2, 5-8.
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Langley, P. (1982).
Language acquisition through error recovery.
Cognition and Brain Theory, 5, 211-255.
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Langley, P. (1982).
A model of early syntactic development.
Proceedings of the 20th Annual Conference of the Society for
Computational Linguistics (pp. 145-151). Toronto, Ontario.
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Langley, P. (1980).
A production system model of first language acquisition.
Proceedings of the Eighth International Conference on Computational
Linguistics (pp. 183-189). Tokyo, Japan.
For more information, send electronic mail to
patrick.w.langley@gmail.com
