Features indicating readability in Swedish text
2013 (English)In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013) / [ed] Stephan Oepen, Kristin Hagen, Janne Bondi Johannesse, Linköping, 2013, 27-40 p.Conference paper (Refereed)
Studies have shown that modern methods of readability assessment, using automated linguistic analysis and machine learning (ML), is a viable road forward for readability classification and ranking. In this paper we present a study of different levels of analysis and a large number of features and how they affect an ML-system’s accuracy when it comes to readability assessment. We test a large number of features proposed for different languages (mainly English) and evaluate their usefulness for readability assessment for Swedish as well as comparing their performance to that of established metrics. We find that the best performing features are language models based on part-of-speech and dependency type.
Place, publisher, year, edition, pages
Linköping, 2013. 27-40 p.
Linköping Electronic Conference Proceedings, ISSN 1650-3686 (print), 1650-3740 (online) ; 85
, NEALT Proceedings, 16
Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:liu:diva-93374ISBN: 978-91-7519-589-6OAI: oai:DiVA.org:liu-93374DiVA: diva2:624380
The 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), May 22–24, 2013, Oslo University, Norway