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47
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 78 (13 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 30 (8 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 29 (11 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.
Linguistic optimization
"... Optimality Theory (OT) is a model of language that combines aspects of generative and connectionist linguistics. It is unique in the field in its use of a rank ordering on constraints, which is used to formalize optimization, the choice of the best of a set of potential linguistic forms. We show tha ..."
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Cited by 17 (3 self)
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Optimality Theory (OT) is a model of language that combines aspects of generative and connectionist linguistics. It is unique in the field in its use of a rank ordering on constraints, which is used to formalize optimization, the choice of the best of a set of potential linguistic forms. We show that phenomena argued to require ranking fall out equally from the form of optimization in OT’s predecessor Harmonic Grammar (HG), which uses numerical weights to encode the relative strength of constraints. We further argue that the known problems for HG can be resolved by adopting assumptions about the nature of constraints that have precedents both in OT and elsewhere in computational and generative linguistics. This leads to a formal proof that if the range of each constraint is a bounded number of violations, HG generates a finite number of languages. This is nontrivial, since the set of possible weights for each constraint is nondenumerably infinite. We also briefly review some advantages of HG. 1
2007) Natural classes are not enough: Biased generalization in novel onset clusters
"... It has long been recognized that a powerful source of information about the phonological knowledge that speakers have comes from the fact that they can generalize this knowledge to novel items. A classic example of this, discussed by Chomsky and Halle (1965) and recited to countless undergraduate cl ..."
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Cited by 11 (1 self)
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It has long been recognized that a powerful source of information about the phonological knowledge that speakers have comes from the fact that they can generalize this knowledge to novel items. A classic example of this, discussed by Chomsky and Halle (1965) and recited to countless undergraduate classes and strangers on airplanes ever since, is the fact that native speakers typically
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 7 (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.
On the Role of Locality in Learning Stress Patterns
, 2008
"... This paper presents a previously unnoticed universal property of stress patterns in the world’s languages: they are, for small neighborhoods, neighborhooddistinct. Neighborhooddistinctness is a locality condition defined in automatatheoretic terms. This universal is established by examining stres ..."
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Cited by 5 (3 self)
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This paper presents a previously unnoticed universal property of stress patterns in the world’s languages: they are, for small neighborhoods, neighborhooddistinct. Neighborhooddistinctness is a locality condition defined in automatatheoretic terms. This universal is established by examining stress patterns contained in two typological studies, Bailey (1995) and Gordon (2002). Strikingly, many logically possible— but unattested—patterns do not have this property. Not only does neighborhooddistinctness unite the attested patterns in a nontrivial way, it also naturally provides an inductive principle allowing learners to generalise from limited data. A learning algorithm is presented which generalises by failing to distinguish sameneighborhood environments perceived in the learner’s linguistic input—hence learning neighborhooddistinct patterns—as well as almost every stress pattern in the typology. In this way, this work lends support to the idea that properties of the learner can explain certain properties of the attested typology, an idea not straightforwardly available in Optimalitytheoretic and Principle and Parameter frameworks.
Variable affix order: grammar and learning
 Lg
, 2010
"... While affix ordering often reflects general syntactic or semantic principles, it can also be arbitrary or variable. This article develops a theory of morpheme ordering based on local morphotactic restrictions encoded as weighted bigram constraints. I examine the formal properties of morphotactic sys ..."
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Cited by 2 (1 self)
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While affix ordering often reflects general syntactic or semantic principles, it can also be arbitrary or variable. This article develops a theory of morpheme ordering based on local morphotactic restrictions encoded as weighted bigram constraints. I examine the formal properties of morphotactic systems, including arbitrariness, nontransitivity, contextsensitivity, analogy, and variation. Several variable systems are surveyed before turning to a detailed corpus study of a variable affix in Tagalog. Bigram morphotactics is shown to cover Tagalog and the typology, while other formalisms, such as alignment, precedence, and position classes, undergenerate. Moreover, learning simulations reveal that affix ordering under bigram morphotactics is subject to analogical pressures, providing a learningtheoretic motivation for the specific patterns of variation observed in Tagalog. I raise a different set of objections to rulebased approaches invoking affix movement. Finally, I demonstrate that bigram morphotactics is restrictive, being unable to generate unattested scenarios such as nonlocal contingency in ordering.*