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A Maximum Entropy Model of Phonotactics and Phonotactic Learning

Bibliographic Data

ID4318239
AuthorsBruce Hayes (Department of Linguistics, UCLA, Los Angeles, CA 90095-1543.), Camille Wilson (0000-0001-7565-0743, Center for Applied Linguistics), Colin Wilson (0000-0001-5355-1533, Department of Linguistics, UCLA, Los Angeles, CA 90095-1543.)
Year2008
Volume39
Issue3
Pages379-440
Publication date2008-07-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueLinguistic Inquiry (JOURNAL)
Journal identifiersISSN: 0024-3892 • E-ISSN: 1530-9150
PublisherThe MIT Press (PUBLISHER • US)
DOI10.1162/ling.2008.39.3.379
OpenAlexW2120321299
LanguageEN
Citations received266
References cited67

The study of phonotactics is a central topic in phonology. We propose a theory of phonotactic grammars and a learning algorithm that constructs such grammars from positive evidence. Our grammars consist of constraints that are assigned numerical weights according to the principle of maximum entropy. The grammars assess possible words on the basis of the weighted sum of their constraint violations. The learning algorithm yields grammars that can capture both categorical and gradient phonotactic patterns. The algorithm is not provided with constraints in advance, but uses its own resources to form constraints and weight them. A baseline model, in which Universal Grammar is reduced to a feature set and an SPE-style constraint format, suffices to learn many phonotactic phenomena. In order for the model to learn nonlocal phenomena such as stress and vowel harmony, it must be augmented with autosegmental tiers and metrical grids. Our results thus offer novel, learning-theoretic support for such representations. We apply the model in a variety of learning simulations, showing that the learned grammars capture the distributional generalizations of these languages and accurately predict the findings of a phonotactic experiment

Context-free grammar · Grammar · L-attributed grammar · Linguistics · Natural language processing · Phonology · Phonotactics · Rule-based machine translation · Speech recognition · Vowel · Vowel harmony · Artificial Intelligence · Computer Science · Mathematics · Music and Audio Processing · Phonetics and Phonology Research · Speech Recognition and Synthesis

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Unique citing works266
Citations per year14
Citation span2007 - 2026 (20)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 150

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