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41
Learning computational grammars
In: http://acl.ldc.upenn.edu/W/W01/W01-0712.pdf (2001)
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42
Learning Computational Grammars
In: http://staff.science.uva.nl/~erikt/papers/conll2001d.ps.gz (2001)
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43
Learning Computational Grammars
In: http://lcg-www.uia.ac.be/lcg/texts/./pdf/nerbonne.conll2001.pdf (2001)
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44
Learning computational grammars
In: http://www.cnts.ua.ac.be/papers/2001/conlld01.pdf (2001)
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45
Learning computational grammars
In: http://www.cnts.ua.ac.be/conll2001/pdf/09704ner.pdf (2001)
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46
Learning computational grammars
In: http://staff.science.uva.nl/~erikt/papers/conll2001d.pdf (2001)
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47
Computational comparison and classification of dialects. Dialectologia et Geolinguistica
In: http://www.let.rug.nl/~heeringa/dialectology/papers/icdg97.pdf (2001)
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48
Null-Headed Nominals in German and English
In: http://grid.let.rug.nl/~nerbonne/papers/null-n-lp-CLIN98.pdf (2000)
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49
Null-Headed Nominals in German and English
In: http://www.let.rug.nl/~nerbonne/papers/null-n-lp-CLIN98.ps (2000)
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50
Computational Comparison and Classification of Dialects
In: http://odur.let.rug.nl/~nerbonne/papers/icdg97.ps (2000)
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51
Learning the Logic of Simple Phonotactics
In: http://lcg-www.uia.ac.be/~erikt/papers/lll2000.ps.gz (2000)
Abstract: We report on experiments which demonstrate that by abductive inference it is possible to learn enough simple phonotactics to distinguish words from non-words for a simplified set of Dutch, the monosyllables. The monosyllables are distinguished in input so that segmentation is not problematic. Frequency information is withheld as is negative data. The methods are all tested using ten-fold cross-validation as well as a fixed number of randomly generated strings. Orthographic and phonetic representations are compared. This paper is part of a larger project comparing different machine learning techniques on linguistic data. 1 Introduction This paper describes an application of abduction to recognising the structure of monosyllabic words. It is part of a larger project comparing various learning methods on language-learning tasks. The learning methods compared in the larger project are taken from three paradigms: stochastic learning (Hidden Markov Models), connectionist learning (simple r.
URL: http://lcg-www.uia.ac.be/~erikt/papers/lll2000.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.8229
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52
Learning the Logic of Simple Phonotactics
In: http://odur.let.rug.nl/~nerbonne/papers/tjong-nerbonne2000.ps (2000)
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53
Change, convergence and divergence among Dutch and Frisian
In: http://www.let.rug.nl/%7Eheeringa/dialectology/papers/ffc99.pdf (2000)
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54
Dutch-German Contact In And Around Bentheim
In: http://grid.let.rug.nl/~nerbonne/papers/bentheim00.ps (2000)
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55
Change, Convergence and Divergence among Dutch and Frisian
In: http://grid.let.rug.nl/~nerbonne/papers/nl-fris-conv-diverg00.ps (2000)
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56
Change, convergence and divergence among Dutch and Frisian
In: http://www.let.rug.nl/~nerbonne/papers/nl-fris-conv-diverg00.pdf (2000)
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57
Connectionist Learning to Read Aloud and Correlation to Human Data
In: http://www.let.rug.nl/~stoianov/ps/CS99_Reading_Stoianov_Stowe_Nerbonne.ps.gz (1999)
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58
Connectionist learning to read aloud and correlation to human data
In: http://odur.let.rug.nl/~nerbonne/papers/cogsci99.pdf (1999)
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59
Learning Simple Phonotactics
In: http://lcg-www.uia.ac.be/~erikt/papers/ijcai99.ps.gz (1999)
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60
Learning Simple Phonotactics
In: http://www.neci.nec.com/~giles/IJCAI99/papers/sang.ps (1999)
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