A multivariate model for classifying texts’ readability
2015 (English)In: Proceedings of the 20th Nordic Conference of Computational Linguistics / [ed] Beáta Megyesi, 2015, 257-261 p.Conference paper (Refereed)
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.
Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:uu:diva-277197DOI: oai:DiVA.org:liu-117611ISBN: 978-91-7519-098-3OAI: oai:DiVA.org:uu-277197DiVA: diva2:904064
The 20th Nordic Conference of Computational Linguistics, NODALIDA 2015
FunderMarcus and Amalia Wallenberg Foundation