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Modelling Productivity with the Gradual Learning Algorithm: The Problem of Accidentally Exceptionless Generalizations

Author(s)
Albright, Adam; Hayes, Bruce
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Abstract
This 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; generalizations
Date issued
2006
URI
https://hdl.handle.net/1721.1/125153
Department
Massachusetts Institute of Technology. Department of Linguistics and Philosophy
Journal
Gradience in Grammar: Generative Perspectives
Publisher
Oxford University Press
Citation
Albright, 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.
Version: Author's final manuscript
ISBN
9780199274796

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