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Learning computational grammars
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In: http://acl.ldc.upenn.edu/W/W01/W01-0712.pdf (2001)
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| 2 |
Learning computational grammars
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In: http://www.cnts.ua.ac.be/papers/2001/conlld01.pdf (2001)
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| 3 |
Learning computational grammars
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In: http://www.cnts.ua.ac.be/conll2001/pdf/09704ner.pdf (2001)
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| 4 |
Learning computational grammars
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In: http://staff.science.uva.nl/~erikt/papers/conll2001d.pdf (2001)
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Learning the Logic of Simple Phonotactics
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In: http://lcg-www.uia.ac.be/~erikt/papers/lll2000.ps.gz (2000)
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| 6 |
Learning the Logic of Simple Phonotactics
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In: http://odur.let.rug.nl/~nerbonne/papers/tjong-nerbonne2000.ps (2000)
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| 7 |
Learning Simple Phonotactics
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In: http://lcg-www.uia.ac.be/~erikt/papers/ijcai99.ps.gz (1999)
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Abstract:
The present paper compares stochastic learning (Hidden Markov Models) , symbolic learning (Inductive Logic Programming), and connectionist learning (Simple Recurrent Networks using backpropagation) on a single, linguistically fairly simple task, that of learning enough phonotactics to distinguish words from non-words for a simplified set of Dutch, the monosyllables. The methods are all tested using 10% reserved data as well as a comparable number of randomly generated strings. Orthographic and phonetic representations are compared. The results indicate that while stochastic and symbolic methods have little difficulty with the task, connectionist methods do. 1 Introduction This paper describes a study of the application of various learning methods for recognizing the structure of monosyllabic words. The learning methods we compare are taken from three paradigms: stochastic learning (Hidden Markov Models), symbolic learning (Inductive Logic Programming), and connectionist learning (Simpl.
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URL: http://lcg-www.uia.ac.be/~erikt/papers/ijcai99.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.8752
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| 8 |
Computational Linguistics and History of Science
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In: http://staff.science.uva.nl/~erikt/papers/sslc2009.pdf
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Computational Linguistics and History of Science
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In: http://odur.let.rug.nl/~nerbonne/papers/kizito-et-al-inf-ext-hist-science-2008.pdf
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