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A multivariate model for classifying texts’ readability
Göteborgs universitet.
Göteborgs universitet.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Educational Sciences, Department of Education. (STOLP)
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Educational Sciences, Department of Education. (STOLP)
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2015 (English)In: Proceedings of the 20th Nordic Conference of Computational Linguistics / [ed] Beáta Megyesi, 2015, 257-261 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2015. 257-261 p.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-277197DOI: oai:DiVA.org:liu-117611ISBN: 978-91-7519-098-3 (print)OAI: oai:DiVA.org:uu-277197DiVA: diva2:904064
Conference
The 20th Nordic Conference of Computational Linguistics, NODALIDA 2015
Funder
Marcus and Amalia Wallenberg Foundation
Available from: 2016-02-17 Created: 2016-02-17 Last updated: 2016-02-17

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Liberg, Carolineaf Geijerstam, ÅsaFolkeryd, Jenny W.

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf