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Convergence of error-driven ranking algorithms*

Published online by Cambridge University Press:  06 September 2012

Giorgio Magri
Giorgio Magri
Affiliation:
CNRS, Université Paris 7
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Abstract

According to the OT error-driven ranking model of language acquisition, the learner performs a sequence of slight re-rankings triggered by mistakes on the incoming stream of data, until it converges to a ranking that makes no more mistakes. Two classical examples are Tesar & Smolensky's (1998) Error-Driven Constraint Demotion (EDCD) and Boersma's (1998) Gradual Learning Algorithm (GLA). Yet EDCD only performs constraint demotion, and is thus shown to predict a ranking dynamics which is too simple from a modelling perspective. The GLA performs constraint promotion too, but has been shown not to converge. This paper develops a complete theory of convergence of error-driven ranking algorithms that perform both constraint demotion and promotion. In particular, it shows that convergent constraint promotion can be achieved (with an error-bound that compares well to that of EDCD) through a proper calibration of the amount by which constraints are promoted.

Type
Articles
Information
Phonology , Volume 29 , Issue 2 , August 2012 , pp. 213 - 269
Copyright
Copyright © Cambridge University Press 2012

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