Stochastic Optimality Theory, local search, and Bayesian learning of hierarchical models
نویسنده
چکیده
The Gradual Learning Algorithm (GLA) (Boersma and Hayes, 2001) can be seen as a stochastic local search method for learning Stochastic OT grammars. This paper tries to achieve the following goals: first, in response to the criticism in (Keller and Asudeh, 2002), we point out that the computational problem of learning stochastic grammars does have a general approximate solution (Lin, 2005). Second, we argue that the Bayesian framework on which the general solution is based connects the perspective of learning a probability distribution over grammars with local search strategies. Third, we also suggest that a general class of hierarchical probabilistic models may be suitable for marrying linguistic formalism with probability distribution.
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