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Acknowledgements This research has been done partially in collaboration with
In: http://people.umass.edu/kjesney/JesneyPaterStaubs2010.pdf (2010)
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Modelling the formation of phonotactic restrictions across the mental lexicon [Online resource]
In: http://user.phil-fak.uni-duesseldorf.de/~hamann/CLSHamannApoussidouBoersma.pdf ; (in:) Proceedings of the 45th meeting of the Chicago linguistics society. - Chicago: 2009 (2009)
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3
The learnability of metrical phonology
In: http://www.lotpublications.nl/publish/articles/002117/bookpart.pdf (2007)
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On-line learning of underlying forms. On-line learning of underlying forms
In: http://fonsg3.hum.uva.nl/paul/papers/On-line learning of UF.pdf (2006)
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Comparing Different Optimality-Theoretic Learning Algorithms:the Case of Metrical Phonology
In: http://www.fon.hum.uva.nl/paul/papers/SS704ApoussidouD.pdf (2004)
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Comparing Different Optimality-Theoretic Learning Algorithms:the Case of Metrical Phonology
In: https://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-05/SS04-05-001.pdf (2004)
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6 AG 4: Learning meets acquisition: the learnability of linguistic frameworks from formal and cognitive perspectives
In: https://dgfs.de/jahrestagung/osnabrueck_2009/dgfs2009-de/files/2008/06/ag04_abstract.pdf
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Conventional OT analysis
In: http://fonsg3.hum.uva.nl/diana/presentation files/Yale2008.pdf
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Modelling the formation of phonotactic restrictions across the mental lexicon
In: http://www.fon.hum.uva.nl/paul/papers/CLSHamaApouBoers.pdf
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10
THE LEARNABILITY OF LATIN STRESS
In: http://www.fon.hum.uva.nl/paul/papers/ApousBoers_IFA25.pdf
Abstract: Optimality-Theoretic learning algorithms are only guaranteed to be successful if the data fed to them contain full structural descriptions of the surface forms, i.e. descriptions that include hidden structure like metrical feet. This is not realistic as a model of acquisition, because children are only exposed to overt forms, e.g. unstructured strings of syllables. Optimality-Theoretic learning algorithms that learn solely from overt forms turn out to sometimes succeed and sometimes fail (Tesar & Smolensky 2000). This possibility of failure is a property of both on-line learning algorithms that have been proposed for OT, namely Error Driven Constraint Demotion (EDCD; Tesar 1995) and the Gradual Learning Algorithm (GLA; Boersma 1997). The possibility of failure is not necessarily bad: one would want an algorithm to fail for languages that do not exist, and to succeed for languages that do exist. Latin exists (or existed). This paper compares the performance of the two learning algorithms for the metrical stress system of Classical Latin. It turns out that EDCD cannot learn this system from overt forms only, and that the GLA can. This suggests that the GLA may be a better model of acquisition than EDCD. The results
URL: http://www.fon.hum.uva.nl/paul/papers/ApousBoers_IFA25.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.1890
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