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51
A maximum entropy model of phonotactics and phonotactic learning
, 2006
"... The study of phonotactics (e.g., the ability of English speakers to distinguish possible words like blick from impossible words like *bnick) 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 ..."
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Cited by 132 (15 self)
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The study of phonotactics (e.g., the ability of English speakers to distinguish possible words like blick from impossible words like *bnick) 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. Possible words are assessed by these grammars based on 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 any 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 SPEstyle constraint format, suffices to learn many phonotactic phenomena. In order to learn nonlocal phenomena such as stress and vowel harmony, it is necessary to augment the model with autosegmental tiers and metrical grids. Our results thus offer novel, learningtheoretic support for such representations. We apply the model to English syllable onsets, Shona vowel harmony, quantityinsensitive stress typology, and the full phonotactics of Wargamay, showing that the learned grammars capture the distributional generalizations of these languages and accurately predict the findings of a phonotactic experiment.
Harmonic grammar with linear programming: From linear . . .
, 2009
"... Harmonic Grammar (HG) is a model of linguistic constraint interaction in which wellformedness is calculated as the sum of weighted constraint violations. We show how linear programming algorithms can be used to determine whether there is a weighting for a set of constraints that fits a set of ling ..."
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Cited by 40 (9 self)
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Harmonic Grammar (HG) is a model of linguistic constraint interaction in which wellformedness is calculated as the sum of weighted constraint violations. We show how linear programming algorithms can be used to determine whether there is a weighting for a set of constraints that fits a set of linguistic data. The associated software package OTHelp provides a practical tool for studying large and complex linguistic systems in the HG framework and comparing the results with those of OT. We first describe the translation from Harmonic Grammars to systems solvable by linear programming algorithms. We then develop an HG analysis of ATR harmony in Lango that is, we argue, superior to the existing OT and rulebased treatments. We further highlight the usefulness of OTHelp, and the analytic power of HG, with a set of studies of the predictions HG makes for phonological typology.
Convergence properties of a gradual learning algorithm for Harmonic Grammar. Rutgers Optimality Archive 970
, 2008
"... Abstract. This paper investigates a gradual online learning algorithm for Harmonic Grammar. By adapting existing convergence proofs for perceptrons, we show that for any nonvarying target language, HarmonicGrammar learners are guaranteed to converge to an appropriate grammar, if they receive compl ..."
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Cited by 39 (14 self)
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Abstract. This paper investigates a gradual online learning algorithm for Harmonic Grammar. By adapting existing convergence proofs for perceptrons, we show that for any nonvarying target language, HarmonicGrammar learners are guaranteed to converge to an appropriate grammar, if they receive complete information about the structure of the learning data. We also prove convergence when the learner incorporates evaluation noise, as in Stochastic Optimality Theory. Computational tests of the algorithm show that it converges quickly. When learners receive incomplete information (e.g. some structure remains hidden), tests indicate that the algorithm is more likely to converge than two comparable OptimalityTheoretic learning algorithms.
Weighted constraints and gradient restrictions on place coccurrence
 in Muna and Arabic. Natural Language and Linguistic Theory
, 2008
"... Abstract. This paper documents a restriction against the cooccurrence of homorganic consonants in the root morphemes of Muna, a western Austronesian language, and compares the Muna pattern with the muchstudied similar pattern in Arabic. As in Arabic, the restriction applies gradiently: its force d ..."
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Cited by 34 (3 self)
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Abstract. This paper documents a restriction against the cooccurrence of homorganic consonants in the root morphemes of Muna, a western Austronesian language, and compares the Muna pattern with the muchstudied similar pattern in Arabic. As in Arabic, the restriction applies gradiently: its force depends on the place of articulation of the consonants involved, and on whether the homorganic consonants are similar in terms of other features. Muna differs from Arabic in the relative strengths of these other features in affecting cooccurrence rates of homorganic consonants. Along with the descriptions of these patterns, this paper presents phonological analyses of the Muna and Arabic patterns in terms of weighted constraints, as in Harmonic Grammar. This account uses a gradual learning algorithm that acquires weights that reflect the relative frequency of different sequence types in the two languages. The resulting grammars assign the sequences acceptability scores that correlate with a measure of their attestedness in the lexicon. This application of Harmonic Grammar illustrates its ability to capture both gradient and categorical patterns.
Linear Optimality Theory as a Model of Gradience in Grammar
 In Gradience in Grammar: Generative Perspectives, ed. Gisbert Fanselow, Caroline Féry, Ralph Vogel, and Matthias Schlesewsky
, 2005
"... This paper provides an overview of Linear Optimality Theory (LOT), a variant of Optimality Theory (OT) designed for the modeling of gradient acceptability judgment data. We summarize the empirical properties of gradient data that have been reported in the experimental literature, and use them to mot ..."
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Cited by 33 (0 self)
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This paper provides an overview of Linear Optimality Theory (LOT), a variant of Optimality Theory (OT) designed for the modeling of gradient acceptability judgment data. We summarize the empirical properties of gradient data that have been reported in the experimental literature, and use them to motivate the design of LOT. We discuss LOT’s notions of constraint competition and optimality, as well as a new formulation of ranking argumentation, which makes it possible to apply standard parameter estimation techniques to LOT. Then the LOT model is compared to Standard OT, to Harmonic Grammar, and to recently proposed probabilisitic versions of OT. 1.
Gradient Grammar: An Effect of Animacy on the Syntax of give in New Zealand and American English
, 2007
"... ..."
Weighted Constraints in Generative Linguistics
 Cognitive Science
, 2009
"... Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) ..."
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Cited by 21 (3 self)
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Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) or as ranks (OT). Weighted constraints have advantages for the construction of accounts of language learning and other cognitive processes, partly because they allow for the adaptation of connectionist and statistical models. HG has been little studied in generative linguistics, however, largely due to influential claims that weighted constraints make incorrect predictions about the typology of natural languages, predictions that are not shared by the more popular OT. This paper makes the case that HG is in fact a promising framework for typological research, and reviews and extends the existing arguments for weighted over ranked constraints. 1
The winner takes it all  almost. cumulativity in grammatical variation. Linguistics
, 2006
"... Classical Optimality Theory in the sense of Prince and Smolensky (2004/1993) implements the intuition that grammars cannot count. The grammaticality of a candidate is fully determined by the ranking of the relevant constraints. Numerical constraint weights play no role. Furthermore, if a competition ..."
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Cited by 19 (0 self)
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Classical Optimality Theory in the sense of Prince and Smolensky (2004/1993) implements the intuition that grammars cannot count. The grammaticality of a candidate is fully determined by the ranking of the relevant constraints. Numerical constraint weights play no role. Furthermore, if a competition between two candidates is decided by a constraint c, it
Natural and Unnatural Constraints in Hungarian Vowel Harmony
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, 2009
"... Phonological constraints can, in principle, be classified according to whether they are natural (founded in principles of Universal Grammar (UG)) or unnatural (arbitrary, learned inductively from the language data). Recent work has used this distinction as the basis for arguments about the role of ..."
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Cited by 18 (1 self)
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Phonological constraints can, in principle, be classified according to whether they are natural (founded in principles of Universal Grammar (UG)) or unnatural (arbitrary, learned inductively from the language data). Recent work has used this distinction as the basis for arguments about the role of UG in learning. Some languages have phonological patterns that arguably reflect unnatural constraints. With experimental testing, one can assess whether such patterns are actually learned by native speakers. Becker, Ketrez, and Nevins (2007), testing speakers of Turkish, suggest that they do indeed go unlearned. They interpret this result with a strong UG position: humans are unable to learn data patterns not backed by UG principles. This article pursues the same research line, locating similarly unnatural data patterns in the vowel harmony system of Hungarian, such as the tendency (among certain stem types) for a final bilabial stop to favor front harmony. Our own test leads to the opposite conclusion to Becker et al.: Hungarians evidently do learn the unnatural patterns. To conclude we consider a bias account—that speakers are able to learn unnatural environments, but devalue them relative to natural ones. We outline a method for testing the strength of constraints as learned by speakers against the strength of the corresponding patterns in the lexicon, and show that it offers tentative support for the hypothesis that unnatural constraints are disfavored by language learners.