Learning bias and phonological-rule induction
Computational Linguistics
Finite-state non-concatenative morphotactics
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The proper treatment of optimality in computational phonology: plenary talk
FSMNLP '09 Proceedings of the International Workshop on Finite State Methods in Natural Language Processing
Priors in Bayesian learning of phonological rules
SIGMorPhon '04 Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology
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A stochastic approach to learning phonology. The model presented captures 7--15% more phonologically plausible underlying forms than a simple majority solution, because it prefers "pure" alternations. It could be useful in cases where an approximate solution is needed, or as a seed for more complex models. A similar process could be involved in some stages of child language acquisition; in particular, early learning of phonotactics.