Priors in Bayesian Learning of Phonological Rules
This paper describes a Bayesian procedure for unsupervised learning of phonological rules from an unlabeled corpus of training data. Like Goldsmith's Linguistica program (Goldsmith, 2004b), whose output is taken as the starting point of this procedure, our learner returns a grammar that consists of a set of signatures, each of which consists of a set of stems and a set of suffixes. Our grammars differ from Linguistica's in that they also contain a set of phonological rules, specifically insertion, deletion and substitution rules, which permit our grammars to collapse far more words into a signature than Linguistica can. Interestingly, the choice of Bayesian prior turns out to be crucial for obtaining a learner that makes linguistically appropriate generalizations through a range of different sized training corpora.