Features indicating readability in Swedish text

• 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.

Subject headings

HUMANIORA  -- Språk och litteratur (hsv//swe)

HUMANITIES  -- Languages and Literature (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

Keyword

Readability assessment

Machine learning

Dependency parsing

Weka

Publication and Content Type

ref (subject category)

kon (subject category)