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Productivity and Reuse in Language : A Theory of Linguistic Computation and Storage /

"Language allows us to express and comprehend an unbounded number of thoughts. This fundamental and much-celebrated property is made possible by a division of labor between a large inventory of stored items (e.g., affixes, words, idioms) and a computational system that productively combines the...

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Detalles Bibliográficos
Autor principal: O'Donnell, Timothy J., 1977- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, England : The MIT Press, [2015]
Colección:Book collections on Project MUSE.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Productivity and Reuse in Language :   |b A Theory of Linguistic Computation and Storage /   |c Timothy J. O'Donnell. 
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505 0 0 |t Introduction --  |t The framework --  |t Formalization of the models and Inference --  |t The english past tense: abstraction and Competition --  |t The english past tense: simulations --  |t English derivational morphology: Productivity, Processing, and Ordering --  |t English derivational morphology: Simulations --  |t Conclusion --  |t Past-tense inflectional classes --  |t Derivational suffixes. 
520 |a "Language allows us to express and comprehend an unbounded number of thoughts. This fundamental and much-celebrated property is made possible by a division of labor between a large inventory of stored items (e.g., affixes, words, idioms) and a computational system that productively combines these stored units on the fly to create a potentially unlimited array of new expressions. A language learner must discover a language's productive, reusable units and determine which computational processes can give rise to new expressions. But how does the learner differentiate between the reusable, generalizable units (for example, the affix -ness, as in coolness, orderliness, cheapness) and apparent units that do not actually generalize in practice (for example, -th, as in warmth but not coolth)? In this book, Timothy O'Donnell proposes a formal computational model, Fragment Grammars, to answer these questions. This model treats productivity and reuse as the target of inference in a probabilistic framework, asking how an optimal agent can make use of the distribution of forms in the linguistic input to learn the distribution of productive word-formation processes and reusable units in a given language"--MIT CogNet. 
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