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Learnability in Optimality Theory (1995)

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by Bruce Tesar , Paul Smolensky
Citations:529 - 35 self
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@TECHREPORT{Tesar95learnabilityin,
    author = {Bruce Tesar and Paul Smolensky},
    title = {Learnability in Optimality Theory},
    institution = {},
    year = {1995}
}

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Abstract

In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subproblem, deducing the constraint ranking particular to a target language, given structural descriptions of positive examples. The structure imposed on the space of possible grammars by Optimality Theory allows efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.

Keyphrases

optimality theory    target language    overt data    possible grammar    effective learnability    linguistic explanation    substantive constraint    general constraint demotion principle    grammar learning    learning procedure    positive example    central subproblem    grammatical structure    correct grammar    learning problem    grammatical module    efficient convergence    present formal result    structural description   

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