On Languages Piecewise Testable in the Strict Sense

Rogers, John H.; Heinz, Jeffrey; Bailey, Gil; Edlefsen, Matt; Visscher, Molly; Wellcome, David; Wibel, Sean

doi:10.1007/978-3-642-14322-9_19

Cited by 96 publications

(66 citation statements)

References 13 publications

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“…As with SL these are closed under intersection but not union, complement, concatenation or Kleene- * . And they form a proper hierarchy in k. The class of SP constraints was characterized by Rogers, et al [23]. Strikingly these are exactly the sets of strings that are closed under subsequence.…”

Section: Strictly Piecewise Testable Setsmentioning

confidence: 99%

Cognitive and Sub-regular Complexity

et al. 2013

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Abstract. We present a measure of cognitive complexity for subclasses of the regular languages that is based on model-theoretic complexity rather than on description length of particular classes of grammars or automata. Unlike description length approaches, this complexity measure is independent of the implementation details of the cognitive mechanism. Hence, it provides a basis for making inferences about cognitive mechanisms that are valid regardless of how those mechanisms are actually realized.

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Section: Strictly Piecewise Testable Setsmentioning

confidence: 99%

Cognitive and Sub-regular Complexity

et al. 2013

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“…The number of parameters, though, rises rapidly-the number of parameters of an LT k distribution is exponential in k. In application, the higher complexity models are likely to be infeasible. On the other hand, there is a complexity hierarchy that parallels the local hierarchy but which is based on precedence-order of symbols independent of the intervening material [23]-which also provides a basis for statistical models. The strictly piecewise distributions, those analogous to n-gram models, are both feasible and are capable of discriminating many longdistance dependencies [29].…”

Section: Statistical Models Of Languagementioning

confidence: 99%

Formal language theory: refining the Chomsky hierarchy

Jäger

Rogers

2012

Phil. Trans. R. Soc. B

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123

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The first part of this article gives a brief overview of the four levels of the Chomsky hierarchy, with a special emphasis on context-free and regular languages. It then recapitulates the arguments why neither regular nor context-free grammar is sufficiently expressive to capture all phenomena in the natural language syntax. In the second part, two refinements of the Chomsky hierarchy are reviewed, which are both relevant to the extant research in cognitive science: the mildly context-sensitive languages (which are located between context-free and context-sensitive languages), and the sub-regular hierarchy (which distinguishes several levels of complexity within the class of regular languages).

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“…The second generalization is similar to the phonological requirement that no word may contain more than one primary stress, which is strictly piecewise (SP; Heinz (2010), Rogers et al (2010)). SP grammars work exactly the same as SL grammar except that instead of illicit substrings they list illicit subsequences.…”

Section: Strictly Piecewisementioning

confidence: 98%

Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Elsner¹,

Kuebler²

2016

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Welcome to the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology. The workshop aims to bring together researchers interested in applying computational techniques to problems in morphology, phonology, and phonetics. Our program this year highlights the ongoing and important interaction between work in computational linguistics and work in theoretical linguistics. We received 23 submissions and accepted 11.The volume of submissions made it necessary to recruit several additional reviewers. We'd like to thank all of these people for agreeing to review papers on what seemed like impossibly short notice.This year also marks the first SIGMORPHON shared task, on morphological reinflection. The shared task received 9 submissions, all of which were accepted, and greatly advanced the state of the art in this area.We thank all the authors, reviewers and organizers for their efforts on behalf of the community. AbstractThis paper conceptualizes speech prosody data mining and its potential application in data-driven phonology/phonetics research. We first conceptualize Speech Prosody Mining (SPM) in a time-series data mining framework. Specifically, we propose using efficient symbolic representations for speech prosody time-series similarity computation. We experiment with both symbolic and numeric representations and distance measures in a series of time-series classification and clustering experiments on a dataset of Mandarin tones. Evaluation results show that symbolic representation performs comparably with other representations at a reduced cost, which enables us to efficiently mine large speech prosody corpora while opening up to possibilities of using a wide range of algorithms that require discrete valued data. We discuss the potential of SPM using time-series mining techniques in future works. IntroductionCurrent investigations on the phonology of intonation and tones (or pitch accent) typically employ data-driven approaches by building research on top of manual annotations of a large amount of speech prosody data (for example, (Morén and Zsiga, 2006; Zsiga and Zec, 2013), and many others). Meanwhile, researchers are also limited by the amount of resources invested in such expensive endeavor of manual annotations. Given this paradox, we believe that this type of data driven approach in phonology-phonetics interface can benefit from tools that can efficiently index, query, classify, cluster, summarize, and discover meaningful prosodic patterns from a large speech prosody corpus.The data mining of f 0 1 (pitch) contour patterns from audio data has recently gained success in the domain of Music Information Retrieval (aka MIR, see (Gulati and Serra, 2014; Gulati et al., 2015; Ganguli, 2015) for examples). In contrast, the data mining of speech prosody f 0 data (here on referred to as Speech Prosody Mining (SPM) 2 ) is a less explored research topic (Raskinis and Kazlauskiene, 2013). Fundamentally, SPM in a large prosody corpus aims at discovering meaningful patterns in the f ...

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On Languages Piecewise Testable in the Strict Sense

Cited by 96 publications

References 13 publications

Cognitive and Sub-regular Complexity

Cognitive and Sub-regular Complexity

Formal language theory: refining the Chomsky hierarchy

Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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