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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Ingår i: Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Linköping Electronic Conference Proceedings 109:33, s. 257-261
NEALT Proceedings Series 23:33, p. 257-261
Publicerad: 2015-05-06
ISBN: 978-91-7519-098-3
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
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.
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