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83
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.
Analytic bias and phonological typology
, 2008
"... Two factors have been proposed as the main determinants of phonological typology: channel bias, phonetically systematic errors in transmission, and analytic bias, cognitive predispositions making learners more receptive to some patterns than others. Much of typology can be explained equally well by ..."
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Cited by 28 (4 self)
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Two factors have been proposed as the main determinants of phonological typology: channel bias, phonetically systematic errors in transmission, and analytic bias, cognitive predispositions making learners more receptive to some patterns than others. Much of typology can be explained equally well by either factor, making them hard to distinguish empirically. This study presents evidence that analytic bias is strong enough to create typological asymmetries in a case where channel bias is controlled. I show that (i) phonological dependencies between the height of two vowels are typologically more common than dependencies between vowel height and consonant voicing, (ii) the phonetic precursors of the heightheight and heightvoice patterns are equally robust and (iii) in two experiments, English speakers learned a heightheight pattern and a voicevoice pattern better than a heightvoice pattern. I conclude that both factors contribute to typology, and discuss hypotheses about their interaction.
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.
GRAMMATICALITY AND UNGRAMMATICALITY IN PHONOLOGY
, 2008
"... In this paper, I make two theoretical claims: (i) For some form to be grammatical in language L, it is not necessary that the form satisfy all constraints that are active in L, i.e. even grammatical forms can violate constraints. (ii) There are degrees of ungrammaticality, i.e. not all ungrammatic ..."
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Cited by 16 (1 self)
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In this paper, I make two theoretical claims: (i) For some form to be grammatical in language L, it is not necessary that the form satisfy all constraints that are active in L, i.e. even grammatical forms can violate constraints. (ii) There are degrees of ungrammaticality, i.e. not all ungrammatical forms are equally ungrammatical. I first show that these claims follow straightforwardly from the basic architecture of an Optimality Theoretic grammar. I then show that the surface sound patterns used most widely in formal phonology cannot be used to test the truth of these two claims, but argue that results from speech processing experiments can. Finally, I discuss three experiments on the processing of nonwords of the form [stVt], [skVk] and [spVp] in English that were designed to test these claims, and show that both claims are confirmed by the results of the experiments.
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 16 (2 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
A maximum entropy model of phonotactics and phonotactic learning
, 2008
"... 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. ..."
Abstract

Cited by 12 (0 self)
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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 SPEstyle 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, learningtheoretic 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.
Structure and substance in artificialphonology learning, Part II: Substance
, 2012
"... Artificial analogues of naturallanguage phonological patterns can often be learned in the lab from small amounts of training or exposure. The difficulty of a featurallydefined pattern has been hypothesized to be affected by two main factors, its formal structure (the abstract logical relationships ..."
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Cited by 12 (3 self)
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Artificial analogues of naturallanguage phonological patterns can often be learned in the lab from small amounts of training or exposure. The difficulty of a featurallydefined pattern has been hypothesized to be affected by two main factors, its formal structure (the abstract logical relationships between the defining features) and its phonetic substance (the concrete phonetic interpretation of the pattern). This paper, the second of a twopart series, reviews the experimental literature on phonetic substance, which is hypothesized to facilitate the acquisition of phonological patterns that resemble naturallyoccurring phonetic patterns. The effects of phonetic substance on pattern learning turn out to be elusive and unreliable in comparison with the robust effects of formal complexity (reviewed in Part I). If naturallanguage acquisition is guided by the same inductive biases as are found in the lab, these results support a theory in which inductive bias shapes only the form, and channel bias shapes the content, of the sound patterns of the worlds languages.