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Hits 1 – 13 of 13

1
Computational Modeling of Nonfinality Effects on Stress Typology
In: Proceedings of the 33. West Coast Conference on Formal Linguistics : [held March 27-29, 2015 at the Simon Fraser University, Vancouver, British Columbia] (2016), S. 371-380
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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2
Learning bias in stress windows: Frequency and attestation
In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2014 Annual Meeting on Phonology ; 2377-3324 (2016)
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3
Sign constraints on feature weights improve a joint model of word segmentation and phonology
Johnson, Mark; Pater, Joe; Staubs, Robert. - : Red Hook, New York : The Association for Computational Linguistics, 2015
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4
Event-Related Potential Evidence of Abstract Phonological Learning in the Laboratory
In: Joe Pater (2015)
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5
Sign Constraints on Feature Weights Improve a Joint Model of Word Segmentation and Phnology
In: Joe Pater (2015)
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6
Emergent Contrast in Agent-Based Modeling of Language
In: Linguistics Department Faculty Publication Series (2015)
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7
Emergent Contrast in Agent-Based Modeling of Language
In: Joe Pater (2015)
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8
Computational Modeling of Learning Biases in Stress Typology
In: Doctoral Dissertations (2014)
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9
Modeling Morphological Subgeneralizations
In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2013 Annual Meeting on Phonology ; 2377-3324 (2014)
Abstract: Exceptions to morphological regularities often pattern together phonologically. In the English past tense, exceptions to the regular ‘Add /-d/’ rule frequently inhabit ‘Islands of Reliability’ (Albright & Hayes, 2003), in which a group of words take the same irregular past and also pattern together on a set of phonological characteristics. Adults seem to have implicit knowledge of both the overall pattern (the regular past) and the ‘subgeneralizations’.We model this knowledge of subgeneralizations through the interaction of a structured lexicon and a Maximum Entropy grammar. Words that pattern together with respect to a particular morphological process are grouped into a ‘bundle’, which is indexed to a constraint expressing the change that these words undergo to realize the morpheme. These ‘operational constraints’ compete with markedness and faithfulness in the phonological component. The phonological regularity of a bundle is represented by the average of constraint violations for members. Novel words are assigned a bundle on the basis of similarity to these averages.Our model shows promising correspondence with human data, including biases toward regularity and Island of Reliability effects. The model’s joint learning approach to phonology and morphology, as well as an inclusive concept of `context’, show promise for future application.
Keyword: English past tense; learning; morphological learning; morphology; phonological learning; productivity
URL: http://journals.linguisticsociety.org/proceedings/index.php/amphonology/article/view/42
https://doi.org/10.3765/amp.v1i1.42
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10
Editors' note
In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2013 Annual Meeting on Phonology ; 2377-3324 (2014)
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11
Disjunction in wh-questions
Haida, Andreas; Repp, Sophie. - : GLSA Publications, 2013
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12
Learning Probabilities Over Underlying Representations
In: Joe Pater (2012)
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13
Specialization Methods and Cataphoricity in Coreference Resolution
Staubs, Robert. - 2009
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