Classic Children’s Literature - Difficult to Read?

  • Dolf Trieschnigg
  • Claudia Hauff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)


Classic children’s literature such as is nowadays freely available thanks to initiatives such as Project Gutenberg. Due to diverging vocabularies and style, these texts are often not readily understandable to children in the present day. Our goal is to make such texts more accessible by aiding children in the reading process, in particular by automatically identifying the terms that result in low readability. As a first step, in this poster we report on a preliminary user study that investigates the extent of the vocabulary problem. We also propose and evaluate a basic approach to detect such difficult terminology.


User Study Unique Term Inter Annotator Agreement Term Distribution Child Today 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Collins-Thompson, K., Callan, J.: Predicting reading difficulty with statistical language models. JASIST 56(13), 1448–1462 (2005)CrossRefGoogle Scholar
  2. 2.
    De Belder, J., Moens, M.F.: Text simplification for children. In: Towards Accessible Search Systems Workshop, pp. 19–26 (2010)Google Scholar
  3. 3.
    Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory 37(1), 145–151 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Petersen, S.E., Ostendorf, M.: A machine learning approach to reading level assessment. Computer Speech and Language 23(1), 89–106 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dolf Trieschnigg
    • 1
  • Claudia Hauff
    • 2
  1. 1.DB groupUniversity of TwenteEnschedeThe Netherlands
  2. 2.HMI groupUniversity of TwenteEnschedeThe Netherlands

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