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Journal article

Probabilistic learning algorithms and optimality theory

Abstract:

This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilistic optimality-theoretic (OT) grammars proposed by Boersma and Hayes (2001). We discuss the limitations of a standard algorithm for OT learning and outline how the GLA attempts to overcome these limitations. We point out a number of serious shortcomings with the GLA: (a) A methodological problem is that the GLA has not been tested on unseen data, which is standard practice in computational langua...

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Publication status:
Published

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Publisher copy:
10.1162/002438902317406704

Authors


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Institution:
University of Oxford
Department:
Oxford, HUM, MML and LPP, Ling Philology and Phonetics
Role:
Author
Journal:
LINGUISTIC INQUIRY
Volume:
33
Issue:
2
Pages:
225-244
Publication date:
2002-01-01
DOI:
EISSN:
0024-3892
ISSN:
0024-3892
URN:
uuid:be0becd2-2d81-4cfb-bfe7-087c0be8ed6b
Source identifiers:
142934
Local pid:
pubs:142934

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