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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