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. 2023 Feb;55(2):491-507.
doi: 10.3758/s13428-022-01802-x. Epub 2022 Mar 16.

A large-scaled corpus for assessing text readability

Scott Crossley  1 , Aron Heintz  2 , Joon Suh Choi  3 , Jordan Batchelor  3 , Mehrnoush Karimi  3 , Agnes Malatinszky  2
Affiliations

A large-scaled corpus for assessing text readability

Scott Crossley et al. Behav Res Methods. 2023 Feb.

Abstract

This paper introduces the CommonLit Ease of Readability (CLEAR) corpus, which provides unique readability scores for ~ 5000 text excerpts along with information about the excerpt's year of publishing, genre, and other metadata. The CLEAR corpus will provide researchers interested in discourse processing and reading with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus includes a number of improvements in comparison to previous readability corpora including size, breadth of the excerpts available, which cover over 250 years of writing in two different genres, and unique readability criterion provided for each text based on teachers' ratings of text difficulty for student readers. This paper discusses the development of the corpus and presents reliability metrics for the human ratings of readability.

Keywords: Corpus linguistics; Natural language processing; Readability; Readability formulas.

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Figures

Fig. 1
Fig. 1
Rater interface for pairwise readability comparisons
Fig. 2
Fig. 2
Correlation plot between Bradley-Terry scores (full and split half) and readability formulas
Fig. 3
Fig. 3
Boxplot for Bradley-Terry scores for excerpts based on genre
Fig. 4
Fig. 4
Scatterplot for Bradley-Terry scores by year of excerpt publication
See this image and copyright information in PMC

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