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dc.contributor.authorAlbright, Adam
dc.contributor.authorHayes, Bruce
dc.date.accessioned2020-05-11T18:31:49Z
dc.date.available2020-05-11T18:31:49Z
dc.date.issued2006
dc.identifier.isbn9780199274796
dc.identifier.urihttps://hdl.handle.net/1721.1/125153
dc.description.abstractThis chapter develops a model that can handle all configurations of gradience and categoricalness. It believes that the solution lies in the trade-off between reliability and generality. It shows how the previous approach to the problem was not enough, and suggests a novel approach using the gradual learning algorithm (GLA), adapted to more general limitations. Keywords: gradual learning algorithm; gradience; generality; prototype; categoricalness; optimality theory; generalizationsen_US
dc.language.isoen
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/acprof:oso/9780199274796.003.0010en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceother univ websiteen_US
dc.titleModelling Productivity with the Gradual Learning Algorithm: The Problem of Accidentally Exceptionless Generalizationsen_US
dc.typeArticleen_US
dc.identifier.citationAlbright, Adam and Bruce Hayes. "Modeling Productivity with the Gradual Learning Algorithm: The Problem of Accidentally Exceptionless Generalizations ." Gradience in Grammar, edited by Gisbert Fanselow, Caroline Féry, Matthias Schlesewsky, and Ralf Vogel, Oxford University Press, 2006.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Linguistics and Philosophyen_US
dc.relation.journalGradience in Grammar: Generative Perspectivesen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/BookItemen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-09-25T16:58:32Z
dspace.date.submission2019-09-25T16:58:33Z
mit.metadata.statusComplete


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