Constraint Demotion is a formal algorithmic method within Optimality Theory that operates by iteratively re-ranking violated constraints lower than those they have failed to satisfy, thereby adjusting the constraint hierarchy to account for observed linguistic outputs.
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2017–2026
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Empirical Tests of the Gradual Learning Algorithm
Paul Boersma, Bruce Hayes · Linguistic Inquiry · 2001 · 1.1K citations · Full text
Learnability in Optimality Theory
Bruce Tesar, Paul Smolensky · Linguistic Inquiry · 1998 · 687 citations
Computational Optimality Theory
Bruce Tesar · Rutgers University Community Repository (Rutgers University) · 1995 · 128 citations · Full text
Andries W. Coetzee · Phonology · 2009 · 128 citations
Convergence of error-driven ranking algorithms
Giorgio Magri · Phonology · 2012 · 37 citations
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Institutions
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Johns Hopkins University
Baltimore, United States
1 h-index
1 publications
687 citations
Université Paris Cité
Paris, France
1 h-index
1 publications
37 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
687 citations
Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
37 citations
University of California, Los Angeles
Los Angeles, United States
1 h-index
1 publications
1.1K citations
University of Colorado Boulder
Boulder, United States
1 h-index
1 publications
128 citations
University of Amsterdam
Amsterdam, The Netherlands
1 h-index
1 publications
1.1K citations
University of Michigan
Ann Arbor, United States
1 h-index
1 publications
128 citations
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- Linguistic Inquiry
3 publications · 1.8K citations
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- Phonology
2 publications · 165 citations
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- Rutgers University Community Repository (Rutgers University)
Repository · 1 publications · 128 citations
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