Restrictiveness in Phonological Grammar and Lexicon Learning

This paper presents a general solution to the problem of learning restrictive phonological grammars and lexicons: Maximum Likelihood Learning of Lexicons and Grammars (MLG; Jarosz 2006). I show that MLG subsumes the effects of standardly used ranking biases and additionally extends to the full phonological learning problem, identifying restrictive grammar and lexicon combinations when learning underlying representations. Rather than using ranking biases to define the relative restrictiveness of multiple analyses of the same data, MLG relies on the likelihood, or probability, that each grammar and lexicon combination assigns to the data. The likelihood provides an explicit measure of how well the grammar and lexicon explain the data, an objective function that may be maximized using a well-known statistical learning algorithm.