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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dc.contributor.author Shieber, Stuart
dc.contributor.author Lappin, Shalom
dc.date.accessioned 2008-08-22T16:28:07Z
dc.date.issued 2007
dc.identifier.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. en
dc.identifier.issn 0022-2267 en
dc.identifier.issn 1469-7742 en
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:2031673
dc.description.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. en
dc.description.sponsorship Engineering and Applied Sciences en
dc.publisher Cambridge University Press en
dc.relation.isversionof http://dx.doi.org/10.1017/S0022226707004628 en
dash.license LAA
dc.title Machine learning theory and practice as a source of insight into universal grammar. en
dc.relation.journal Journal of Linguistics en
dash.depositing.author Shieber, Stuart

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  • FAS Scholarly Articles [6463]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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