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Fragment Grammars: Exploring Computation and Reuse in Language

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dc.contributor.advisor Joshua Tenenbaum
dc.contributor.author O'Donnell, Timothy J. en_US
dc.contributor.author Tenenbaum, Joshua B. en_US
dc.contributor.author Goodman, Noah D. en_US
dc.contributor.other Computational Cognitive Science en_US
dc.date.accessioned 2009-03-31T05:00:03Z
dc.date.available 2009-03-31T05:00:03Z
dc.date.issued 2009-03-31
dc.identifier.uri http://hdl.handle.net/1721.1/44963
dc.description.abstract Language relies on a division of labor between stored units and structure building operations which combine the stored units into larger structures. This division of labor leads to a tradeoff: more structure-building means less need to store while more storage means less need to compute structure. We develop a hierarchical Bayesian model called fragment grammar to explore the optimum balance between structure-building and reuse. The model is developed in the context of stochastic functional programming (SFP) and in particular using a probabilistic variant of Lisp known as the Church programming language (Goodman, Mansinghka, Roy, Bonawitz, & Tenenbaum, 2008). We show how to formalize several probabilistic models of language structure using Church, and how fragment grammar generalizes one of them---adaptor grammars (Johnson, Griffiths, & Goldwater, 2007). We conclude with experimental data with adults and preliminary evaluations of the model on natural language corpus data. en_US
dc.format.extent 63 p. en_US
dc.relation.ispartofseries MIT-CSAIL-TR-2009-013 en_US
dc.subject Language en_US
dc.subject Stochastic Functional Programming en_US
dc.subject Stochastic Memoization en_US
dc.subject Reuse en_US
dc.subject Lexicon en_US
dc.subject Hierarchical Bayes en_US
dc.title Fragment Grammars: Exploring Computation and Reuse in Language en_US
dc.identifier.citation O'DONNELL, T., GOODMAN, N., and TENENBAUM, J. 2009. Fragment Grammars: Exploring Computation and Reuse in Language. MIT Computer Science and Artificial Intelligence Laboratory Technical Report Series, MIT-CSAIL-TR-2009-013.


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