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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Learning computational grammars John Nerbonne; Anja Belz; Nicola Cancedda... In: http://acl.ldc.upenn.edu/W/W01/W01-0712.pdf (2001) BASE Show details 2 Learning computational grammars John Nerbonne; Anja Belz; Nicola Cancedda... In: http://www.cnts.ua.ac.be/papers/2001/conlld01.pdf (2001) BASE Show details 3 Learning computational grammars John Nerbonne; Anja Belz; Nicola Cancedda... In: http://www.cnts.ua.ac.be/conll2001/pdf/09704ner.pdf (2001) BASE Show details 4 Learning computational grammars John Nerbonne; Anja Belz; Nicola Cancedda... In: http://staff.science.uva.nl/~erikt/papers/conll2001d.pdf (2001) BASE Show details 5 Learning the Logic of Simple Phonotactics Erik F. Tjong; Kim Sang; John Nerbonne 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 BASE Hide details 6 Learning the Logic of Simple Phonotactics Erik F. Tjong; Kim Sang; John Nerbonne In: http://odur.let.rug.nl/~nerbonne/papers/tjong-nerbonne2000.ps (2000) BASE Show details 7 Learning Simple Phonotactics Erik F. Tjong; Kim Sang; John Nerbonne In: http://lcg-www.uia.ac.be/~erikt/papers/ijcai99.ps.gz (1999) BASE Show details 8 Computational Linguistics and History of Science John Kizito; Ismail Fahmi; Erik Tjong... In: http://staff.science.uva.nl/~erikt/papers/sslc2009.pdf BASE Show details 9 Computational Linguistics and History of Science John Kizito; Ismail Fahmi; Erik Tjong... In: http://odur.let.rug.nl/~nerbonne/papers/kizito-et-al-inf-ext-hist-science-2008.pdf BASE Show details

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