Richness of the Base and Probabilistic Unsupervised Learning in Optimality Theory
Jarosz, Gaja. 2006. Richness of the Base and Probabilistic Unsupervised Learning in Optimality Theory. Association for Computational Linguistics: Proceedings of the Eighth Meeting of the ACL Special Interest Group in Computational Phonology.
This paper proposes an unsupervised learning algorithm for Optimality Theo- retic grammars, which learns a complete constraint ranking and a lexicon given only unstructured surface forms and mor- phological relations. The learning algo- rithm, 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 con- structed language systems with different types of hidden structure: voicing neu- tralization, 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 iden- tify correct, restrictive grammars is due in part to its explicit reliance on the Opti- mality Theoretic notion of Richness of the Base.