Optimality-Theoretic learning in the Praat program
Paul Boersma · 1999
This tutorialyield a step-by-step introd-step to stochastic OT grammarsand about how you can use theGradxW Learning Algorithm available in the Praat program to help you rank Optimality-Theoretic constraints inordCx0 and stochastic grammars. This tutorial dtorial) how you can dn) Optimality-Theoretic tableausand simulate Optimality-Theoretic learning with the Praat program (Boersma & Weenink 1992-2000). 1. Kinds of OT grammars Accord1) to Prince & Smolensky (1993), anOptimality-T 0 0 0 (OT grammar consists of a number of ranked constraints. For every possible input (undW8;)dC)d , GEN generates a (possibly very large number of output candidates, and the ranking ordki of the constraints the winning candg )E08 which becomes the single optimal output. In OT, ranking is strict, i.e., if a constraint A is ranked&)dnstraint )E&xx8)kE&N&&)d B, C, and D, a candEC)k0 that violates only constraint A will always be beaten by any cand&8WN)k0;W&&)d&)d& A (andk0Cx&&)d)d&)d&&)dx...