Machine learning theory and practice as a source of insight into universal grammar.

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Machine learning theory and practice as a source of insight into universal grammar.

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Title: Machine learning theory and practice as a source of insight into universal grammar.
Author: Shieber, Stuart; Lappin, Shalom

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Citation: Shalom Lappin and Stuart M. Shieber. Machine learning theory and practice as a source of insight into universal grammar. Journal of Linguistics, 43(2):393-427, 2007.
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Abstract: In this paper, we explore the possibility that machine learning approaches to natural-language processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.
Published Version: http://dx.doi.org/10.1017/S0022226707004628
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:2031673

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  • FAS Scholarly Articles [7219]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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