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Title:Learning OT Constraint Rankings Using a Maximum Entropy Model
Authors: Sharon Goldwater ; Mark Johnson
Date: 2003
Publication Title:Proceedings of the Workshop on Variation withing Optimality Theory, Stockholm University, 2003
Publication Type:Conference Paper Publication Status:Published
Abstract:
A weakness of standard Optimality Theory is its inability to account for grammers with free variation. We describe here the Maximum Entropy model, a general statistical model, and show how it can be applied in a contraint-based linguistic framework to model and learn grammers with free variation, as well as categorical grammers. We report the results of using the MaxEnt model for learning two different grammers: one with variation, and one without. Our results are as good as those of a previous probabilistic version of OT, the Gradual Learning Algorithm (Boersma, 1997), and we argue that our model is more general and mathematically well-motivated.
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Bibtex format
@InProceedings{EDI-INF-RR-1204,
author = { Sharon Goldwater and Mark Johnson },
title = {Learning OT Constraint Rankings Using a Maximum Entropy Model},
book title = {Proceedings of the Workshop on Variation withing Optimality Theory, Stockholm University, 2003},
year = 2003,
url = {http://www.cog.brown.edu/~mj/papers/GoldwaterJohnson03.pdf},
}


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