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ACL
1994

A Markov Language Learning Model for Finite Parameter Spaces

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A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explicitly calculated sample complexity learning times under different input distribution assumptions (including CHILDES database language input) and learning regimes. We also briefly describe a new way to formally model (rapid) diachronic syntax change. BACKGROUND MOTIVATION: TRIGGERS AND LANGUAGE ACQUISITION Recently, several researchers, including Gibson and Wexler (1994), henceforth GW, Dresher and Kaye (1990); and Clark and Roberts (1993) have modeled language learning in a (finite) space whose grammars are characterized by a finite number of parameters or nlength Boolean-valued vectors. Many current linguistic theories now employ such parametric models explicitly or in spirit, including Lexical-Functional Grammar and versions of HPSG, besides GB variants. With all such models, key questions about sample comple...
Partha Niyogi, Robert C. Berwick
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1994
Where ACL
Authors Partha Niyogi, Robert C. Berwick
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