Modelling Productivity with the Gradual Learning Algorithm: The Problem of Accidentally Exceptionless Generalizations
Name
AlbrightHayesModelingProductivityWithGLA.pdf
Description
Accepted version
Size
245.03 KB
Format
Adobe PDF
Checksum (MD5)
d7f7a660e19087e0ff631623479015dc
Author(s)
Albright, Adam
Hayes, Bruce
Date Issued
2006
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
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
MIT Department
Massachusetts Institute of Technology. Department of Linguistics and Philosophy
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/acprof:oso/9780199274796.003.0010
