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The Credit Problem in parametric stress: A probabilistic approach
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In: Linguistics Department Faculty Publication Series (2021)
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| 2 |
Modeling the Acquisition of Phonological Interactions: Biases and Generalization
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2020 Annual Meeting on Phonology ; 2377-3324 (2021)
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Preface: SCiL 2020 Editors' Note
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In: Proceedings of the Society for Computation in Linguistics (2020)
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| 4 |
Learning Exceptionality and Variation with Lexically Scaled MaxEnt ...
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| 5 |
Learning Exceptionality and Variation with Lexically Scaled MaxEnt
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In: Proceedings of the Society for Computation in Linguistics (2019)
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| 6 |
Evaluating Domain-General Learning of Parametric Stress Typology
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In: Proceedings of the Society for Computation in Linguistics (2019)
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| 7 |
Learning syntactic parameters without triggers by assigning credit and blame
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In: Joe Pater (2019)
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| 8 |
Preface: SCiL 2019 Editors’ Note
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Preface: SCiL 2018 Editors’ Note
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 10 |
Learning Parametric Stress without Domain-Specific Mechanisms
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2016 Annual Meeting on Phonology ; 2377-3324 (2017)
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| 11 |
Sonority Sequencing in Polish: the Combined Roles of Prior Bias & Experience
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2016 Annual Meeting on Phonology ; 2377-3324 (2017)
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| 12 |
Learning Opaque and Transparent Interactions in Harmonic Serialism
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2015 Annual Meeting on Phonology ; 2377-3324 (2016)
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| 13 |
Probabilistic Selection of Input in Morphophonological Acquisition
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Abstract:
Degree: Doctor of Philosophy ; Abstract: This dissertation sets out to explain the development during morphophonological acquisition and its possible learning outcomes by constructing a Probabilistic Selection of Input (PSI) rich lexicon learning model, in part based on psycholinguistic evidence that rich language details are lexically encoded. Contrary to traditional UR assumptions based on lexical economy, PSI stores and associates all surface allomorphs of a morpheme in a rich lexicon as possible inputs of the morpheme. Through the lexical associations between stored allomorphs, the leaerner assigns a probability between 0 and 1 to each allomorph and probabilistically select the phonological input of the morpheme. Output pattern variation along the acquisition course is thus analyzed as results of different input preferences and corresponding grammar shifts at sequential stages, and a successful morphophonological learning stands for an adult-like lexical generalization (i.e. input probabilities) and phonological grammar captured by learners. Diachronic morphophonemic changes can nevertheless occur with a shift in input preferences over learning generations, which gradually leads to permanent grammar shifts. PSI is tested with computer simulations using corpus data as a training corpus in various case studies, including the acquisition of Dutch stem-final voicing alternation (Chapter 3), the diachronic change of Mandarin Tone 3 (Chapter 4), and the emergence of Korean stem-final obstruent variations (Chapter 5). Learning outcomes similar to the performance by native speakers in elicitation tasks are demonstrated in the PSI simulations as a result of temporarily or permanently selecting different stored allomorphs as phonological inputs.
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Keyword:
computer modeling; Dutch; hybrid model; Korean; language acquisition; Mandarin; morphophonology
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URL: http://hdl.handle.net/10402/era.39225
https://era.library.ualberta.ca/files/wm117p249
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| 14 |
Probabilistic Selection of Input in Morphophonological Acquisition
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| 15 |
Serial Markedness Reduction
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2013 Annual Meeting on Phonology ; 2377-3324 (2014)
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| 17 |
Rich Lexicons and Restrictive Grammars: Maximum Likelihood Learning in Optimality Theory ...
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| 18 |
Rich Lexicons and Restrictive Grammars: Maximum Likelihood Learning in Optimality Theory
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| 19 |
Polish Yers and the Finer Structure of Output-Output Correspondence
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In: Annual Meeting of the Berkeley Linguistics Society; BLS 31: General Session and Parasession on Prosodic Variation and Change; 181-192 ; 2377-1666 ; 0363-2946 (2005)
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