Computational optimality theory
Computational optimality theory
Unsupervised learning of the morphology of a natural language
Computational Linguistics
Inside-outside reestimation from partially bracketed corpora
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A Bayesian model for morpheme and paradigm identification
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Bayesian learning over conflicting data: predictions for language change
SigMorPhon '08 Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology
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This paper proposes an unsupervised learning algorithm for Optimality Theoretic grammars, which learns a complete constraint ranking and a lexicon given only unstructured surface forms and morphological relations. The learning algorithm, which is based on the Expectation-Maximization algorithm, gradually maximizes the likelihood of the observed forms by adjusting the parameters of a probabilistic constraint grammar and a probabilistic lexicon. The paper presents the algorithm's results on three constructed language systems with different types of hidden structure: voicing neutralization, stress, and abstract vowels. In all cases the algorithm learns the correct constraint ranking and lexicon. The paper argues that the algorithm's ability to identify correct, restrictive grammars is due in part to its explicit reliance on the Optimality Theoretic notion of Richness of the Base.