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21
Learning Phonology With Substantive Bias: An Experimental and Computational Study of Velar Palatalization
, 2006
"... There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments test ..."
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Cited by 84 (2 self)
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There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. Participants were found to generalize velar palatalization (e.g., the change from [k]asinkeep to [t�ʃ]asincheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e]asincape) to the high front vowel context (i.e., before [i]asin keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed.
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.
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
Locality in metrical typology
, 2009
"... Recent work in metrical typology within Optimality Theory has emphasised the rhythmic distribution of stress peaks by reference to clashes and lapses, compared to the more central role of foot constituency characteristic of most previous approaches. One consequence of this emphasis has been the intr ..."
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Cited by 10 (4 self)
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Recent work in metrical typology within Optimality Theory has emphasised the rhythmic distribution of stress peaks by reference to clashes and lapses, compared to the more central role of foot constituency characteristic of most previous approaches. One consequence of this emphasis has been the introduction of constraints that require reference to nonadjacent objects in the representation, such as two unstressed syllables plus a word edge or a stress peak. I argue here for a constraintbased approach to metrical typology that permits only strictly local formulations. This approach requires increased reference to foot structure, while maintaining local reference to clashes and lapses. The revised set of constraints predicts a larger set of possible stress systems, but correctly includes an attested iambic pattern excluded by recent theories.
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 8 (4 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.
Counting Rankings
"... In this paper, I present a recursive algorithm that calculates the number of rankings that are consistent with a set of data (i.e. optimal candidates) in the framework of Optimality Theory. The ability to compute this quantity, which I call the rvolume, makes possible a simple and effective Bayesia ..."
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Cited by 4 (1 self)
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In this paper, I present a recursive algorithm that calculates the number of rankings that are consistent with a set of data (i.e. optimal candidates) in the framework of Optimality Theory. The ability to compute this quantity, which I call the rvolume, makes possible a simple and effective Bayesian heuristic in learning – all else equal, choose the candidate preferred by the highest number of constraint rankings consistent with previous observations. Using this heuristic, I formulate a learning algorithm that is guaranteed to make fewer than k log 2 k errors while learning rankings of k constraints. This loglinear mistake bound is an improvement over the quadratic mistake bound of Recursive Constraint Demotion and is within a logarithmic factor of the best possible mistake bound for OT learning. I conclude with an illustration of learning syllable structure grammars to contrast the learning curves with the mistake bounds and computational requirements of several ranking algorithms. 1
2009b) Generating Contenders
"... In Optimality Theory, a contender is a candidate that is optimal under some ranking of the constraints. When the candidate generating function Gen and all of the constraints are rational (i.e., representable with (weighted) finite state automata) it is possible to generate the entire set of contende ..."
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Cited by 3 (3 self)
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In Optimality Theory, a contender is a candidate that is optimal under some ranking of the constraints. When the candidate generating function Gen and all of the constraints are rational (i.e., representable with (weighted) finite state automata) it is possible to generate the entire set of contenders for a given input form in much the same way that optima for a single ranking are generated. This paper gives a brief introduction to rational constraints and provides an algorithm for generating contenders whose complexity, modulo the number of contenders generated, is linear in the length of the underlying form with a multiplicative constant representing the size of the finitestate representation of the constraint set. 1
Using Entropy to Learn OT Grammars From Surface Forms Alone
"... The problem of ranking a set of constraints in Optimality Theory (Prince and Smolensky 1993) in ..."
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Cited by 2 (0 self)
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The problem of ranking a set of constraints in Optimality Theory (Prince and Smolensky 1993) in