<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Text readability metrics assess how much effort a reader must put into comprehending a given text. They are, e.g., used to choose appropriate readings for different student proficiency levels, or to make sure that crucial information is efficiently conveyed (e.g., in an emergency). Flesch Reading Ease is such a globally used formula that it is even integrated into the MS Word Processor. However, its constants are language-dependent. The original formula was created for English. So far it has been adapted to several European languages, Bangla, and Hindi. This paper describes the Czech adaptation, with the language-dependent constants optimized by a machine-learning algorithm working on parallel corpora of Czech and English, Russian, Italian, and French, respectively.</p>
</abstract>

Text readability metrics assess how much effort a reader must put into comprehending a given text. They are, e.g., used to choose appropriate readings for different student proficiency levels, or to make sure that crucial information is efficiently conveyed (e.g., in an emergency). Flesch Reading Ease is such a globally used formula that it is even integrated into the MS Word Processor. However, its constants are language-dependent. The original formula was created for English. So far it has been adapted to several European languages, Bangla, and Hindi. This paper describes the Czech adaptation, with the language-dependent constants optimized by a machine-learning algorithm working on parallel corpora of Czech and English, Russian, Italian, and French, respectively.

Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech

Journal of Linguistics/Jazykovedný casopis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech"}

Text readability metrics assess how much effort a reader must put into comprehending a given text. They are, e.g., used to choose appropriate readings for...

Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech

Published Online: Dec 30, 2021

Page range: 477 - 487

DOI: https://doi.org/10.2478/jazcas-2021-0044

Keywords
complexity, parallel corpus, Czech, Flesch Reading Ease, machine learning

© 2021 Klára Bendová, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech

Published Online: Dec 30, 2021

Page range: 477 - 487

DOI: https://doi.org/10.2478/jazcas-2021-0044

Keywordscomplexity, parallel corpus, Czech, Flesch Reading Ease, machine learning

© 2021 Klára Bendová, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
complexity, parallel corpus, Czech, Flesch Reading Ease, machine learning