A multivariate model for classifying texts’ readability

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

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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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