Article | Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania | A multivariate model for classifying texts’ readability
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Title:
A multivariate model for classifying texts’ readability
Author:
Katarina Heimann M ühlenbock: Department of Swedish, University of Gothenburg, Sweden Sofie Johansson Kokkinakis: Department of Swedish, University of Gothenburg, Sweden Caroline Liberg: Department of Education, Uppsala University, Sweden Åsa af Geijerstam: Department of Education, Uppsala University, Sweden Jenny Wiksten Folkeryd: Department of Education, Uppsala University, Sweden Arne Jönsson: Department of Computer and Information Science, Link¬®oping University, Sweden Erik Kanebrant: Department of Computer and Information Science, Link¬®oping University, Sweden Johan Falkenjack: Department of Computer and Information Science, Link¬®oping University, Sweden
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Full text (pdf)
Year:
2015
Conference:
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Issue:
109
Article no.:
033
Pages:
257-261
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2015-05-06
ISBN:
978-91-7519-098-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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We report on results from using the multivariate readability model SVIT to classify texts into various levels. We investigate how the language features integrated in the SVIT model can be transformed to values on known criteria like vocabulary, grammatical fluency and propositional knowledge. Such text criteria, sensitive to content, readability and genre in combination with the profile of a student’s reading ability form the base to individually adapted texts. The procedure of levelling texts into different stages of complexity is presented along with results from the first cycle of tests conducted on 8th grade students. The results show that SVIT can be used to classify texts into different complexity levels.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Katarina Heimann M ühlenbock, Sofie Johansson Kokkinakis, Caroline Liberg, Åsa af Geijerstam, Jenny Wiksten Folkeryd, Arne Jönsson, Erik Kanebrant, Johan Falkenjack
Title:
A multivariate model for classifying texts’ readability
References:

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Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Katarina Heimann M ühlenbock, Sofie Johansson Kokkinakis, Caroline Liberg, Åsa af Geijerstam, Jenny Wiksten Folkeryd, Arne Jönsson, Erik Kanebrant, Johan Falkenjack
Title:
A multivariate model for classifying texts’ readability
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