Efficient Learning of Tier-Based Strictly k-Local Languages

Jardine, Adam; McMullin, Kevin

doi:10.1007/978-3-319-53733-7_4

“…13 Secondly, the learners' apparent preference for strictly transvocalic over unbounded locality (when faced with ambiguous evidence) translates into a preference for hypothesizing a tier containing all consonants over one that contains only a subset of consonants (namely liquids), other things being equal. Interestingly, the formal learning algorithm presented by Jardine and McMullin (2017), which is proven to be successful for the entire TSL k class (i.e., for any given k), begins with the hypothesis that T = Σ (see also Jardine & Heinz, 2016). It then removes segments from the tier one-by-one when possible, on the basis of overt evidence that they do not themselves figure in R and that co-occurrence restrictions hold across them (i.e., that they are transparent, not blockers).…”

Section: Formal Language Theorymentioning

confidence: 99%

Inductive learning of locality relations in segmental phonology

McMullin

¹

,

Hansson

²

2019

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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This paper reports on a series of artificial grammar learning experiments focused on locality relations in patterns of long-distance consonant agreement (harmony) and disagreement (dissimilation). Participants in experimental conditions were exposed to dependencies affecting stem-suffix pairs of liquids at either a short-range (transvocalic, CVCVLV-LV) or medium-range (beyond-transvocalic, CVLVCV-LV) distance. Two experiments used a poverty of stimulus paradigm, offering no information about the other distance level; participants interpreted short-range interaction as a strictly transvocalic dependency but medium-range interaction as unbounded, generalizing to other distances. Two experiments employed a 'rich stimulus' paradigm, where training data unambiguously indicated the absence of any dependency at the other distance; this enabled probing of specific locality patterns, in particular strictly beyond-transvocalic dependencies. The constraint-based Agreement by Correspondence model of non-adjacent consonant interactions predicts such patterns to be possible for dissimilation but not harmony. The results do not support this hypothesis: Participants seem to have serious difficulty learning strictly beyond-transvocalic dependencies of either kind. Our findings are more consistent with recent proposals that the space of learnable phonotactic restrictions is delimited by the Tier-based Strictly 2-Local class of formal languages. Strictly transvocalic and unbounded dependencies lie within this region, whereas strictly beyond-transvocalic dependencies are more complex, falling outside the learner's hypothesis space.Practice CVCV (8) CVCV-li (8) CVCV-ɹu (8) 24Training CVCVLV (96) CVCVlV-li (96) CVCVɹV-li (96) CVCVlV-ɹu (96) CVCVɹV-ɹu (96) 480 CVLVCV (96) CVlVCV-li (96) CVɹVCV-li (96) CVlVCV-ɹu (96) CVɹVCV-ɹu (96) 480 CVCVCV (96) CVCVCV-li (96) CVCVCV-ɹu (96) 288Testing CVCVLV (32) CVCVlV-li (16) CVCVɹV-li (16) CVCVlV-ɹu (16) CVCVɹV-ɹu (16) 96 CVLVCV (32) CVlVCV-li (16) CVɹVCV-li (16) CVlVCV-ɹu (16) CVɹVCV-ɹu (16) 96 LVCVCV (32) lVCVCV-li (16) ɹVCVCV-li (16) lVCVCV-ɹu (16) ɹVCVCV-ɹu (16) 96 Total = 1560

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“…We contend that this view is overly generous and that tighter bounds can be established, at least for specific subparts of morphology. Morphotactics defines the restrictions on the possible orderings of morphological units, and we argued based on data from typologically diverse languages that the power of natural language morphotactics is severely restricted:128 Subregular Morphotactics All morphotactic dependencies are tier-based strictly local.In contrast to regular languages, tier-based strictly local languages are efficiently learnable in the limit from positive text (Heinz et al, 2012;Jardine and Heinz, 2016). Our result thus marks a first step towards provably correct machine learning algorithms for natural language morphology.…”

mentioning

confidence: 94%

“…The subregular hierarchy includes many other classes (see Fig. 1), but the previous three are noteworthy because they are conceptually simple and efficiently learnable in the limit from positive data (Heinz et al, 2012;Jardine and Heinz, 2016) while also furnishing sufficient power for a wide range of phonological phenomena (Heinz, 2015;Jardine, 2015).In this section, we investigate the role of strictly local, strictly piecewise and tier-based strictly local patterns in morphotactics. We show that some but not all patterns are strictly local or strictly piecewise, whereas all typologically instantiated patterns seem to fit in the class of tier-based strictly local languages.…”

mentioning

confidence: 99%

Automatic Detection of Intra-Word Code-Switching

Nguyen¹,

Cornips²

2016

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

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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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“…In contrast to regular languages, tier-based strictly local languages are efficiently learnable in the limit from positive text (Heinz et al, 2012;Jardine and Heinz, 2016). Our result thus marks a first step towards provably correct machine learning algorithms for natural language morphology.…”

Section: Resultsmentioning

confidence: 78%

“…The subregular hierarchy includes many other classes (see Fig. 1), but the previous three are noteworthy because they are conceptually simple and efficiently learnable in the limit from positive data (Heinz et al, 2012;Jardine and Heinz, 2016) while also furnishing sufficient power for a wide range of phonological phenomena (Heinz, 2015;Jardine, 2015).…”

Section: Subregular Patterns In Morphologymentioning

confidence: 99%

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

Elsner¹,

Kuebler²

2016

0

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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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Efficient Learning of Tier-Based Strictly k-Local Languages

Cited by 60 publications

References 10 publications

Inductive learning of locality relations in segmental phonology

Inductive learning of locality relations in segmental phonology

Automatic Detection of Intra-Word Code-Switching

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

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