- 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.
- Links To Paper
- 1st link
- 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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