A Posteriori Agreement as a Quality Measure for Readability Prediction Systems

  • Philip van Oosten
  • Véronique Hoste
  • Dries Tanghe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6609)

Abstract

All readability research is ultimately concerned with the research question whether it is possible for a prediction system to automatically determine the level of readability of an unseen text. A significant problem for such a system is that readability might depend in part on the reader. If different readers assess the readability of texts in fundamentally different ways, there is insufficient a priori agreement to justify the correctness of a readability prediction system based on the texts assessed by those readers. We built a data set of readability assessments by expert readers. We clustered the experts into groups with greater a priori agreement and then measured for each group whether classifiers trained only on data from this group exhibited a classification bias. As this was found to be the case, the classification mechanism cannot be unproblematically generalized to a different user group.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, R.C., Davison, A.: Conceptual and Empirical Bases of Readability Formulas. Tech. Rep. 392, University of Illinois at Urbana-Champaign (October 1986)Google Scholar
  2. 2.
    Beigman Klebanov, B., Beigman, E.: From Annotator Agreement to Noise Models. Computational Linguistics 35(4), 495–503 (2009)CrossRefGoogle Scholar
  3. 3.
    van den Bosch, A., Busser, B., Canisius, S., Daelemans, W.: An efficient memory-based morphosyntactic tagger and parser for dutch. In: van Eynde, F., Dirix, P., Schuurman, I., Vandeghinste, V. (eds.) Proceedings of CLIN17, pp. 99–114 (2007)Google Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  5. 5.
    Coleman, M., Liau, T.L.: A computer readability formula designed for machine scoring. Journal of Applied Psychology 60, 283–284 (1975)CrossRefGoogle Scholar
  6. 6.
    Flesch, R.: A new readability yardstick. Journal of Applied Psychology 32(3), 221–233 (1948)CrossRefGoogle Scholar
  7. 7.
    Heilman, M.J., Collins-Thompson, K., Callan, J., Eskenazi, M.: Combining lexical and grammatical features to improve readability measures for first and second language texts. In: Proceedings of HLT (2007)Google Scholar
  8. 8.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  9. 9.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice-Hall, Englewood Cliffs (2008)Google Scholar
  10. 10.
    Kate, R.J., Luo, X., Patwardhan, S., Franz, M., Florian, R., Mooney, R.J., Roukos, S., Welty, C.: Learning to Predict Readability using Diverse Linguistic Features. In: Proceedings of Coling23 (2010)Google Scholar
  11. 11.
    McNamara, D.S., Kintsch, E., Songer, N.B., Kintsch, W.: Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Tech. rep., University of Colorado (1993)Google Scholar
  12. 12.
    van Noord, G.J.: Large Scale Syntactic Annotation of written Dutch (LASSY) (January 2009), http://www.let.rug.nl/vannoord/Lassy/
  13. 13.
    van Oosten, P., Tanghe, D., Hoste, V.: Towards an Improved Methodology for Automated Readability Prediction. In: Proceedings of LREC7 (2010)Google Scholar
  14. 14.
    Pitler, E., Nenkova, A.: Revisiting readability: A unified framework for predicting text quality. In: EMNLP, pp. 186–195. ACL (2008)Google Scholar
  15. 15.
    Schuurman, I., Hoste, V., Monachesi, P.: Cultivating Trees: Adding Several Semantic Layers to the Lassy Treebank in SoNaR. In: Proceedings of TLT7. Groningen, The Netherlands (2009)Google Scholar
  16. 16.
    Schwarm, S.E., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Proceedings of ACL43, pp. 523–530. Association of Computational Linguistics, Ann Arbor (June 2005)Google Scholar
  17. 17.
    Staphorsius, G.: Leesbaarheid en leesvaardigheid. De ontwikkeling van een domeingericht meetinstrument. Cito, Arnhem (1994)Google Scholar
  18. 18.
    Tanaka-Ishii, K., Tezuka, S., Terada, H.: Sorting texts by readability. Computational Linguistics 36(2), 203–227 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Philip van Oosten
    • 1
    • 2
  • Véronique Hoste
    • 1
    • 2
  • Dries Tanghe
    • 1
    • 2
  1. 1.LT3 Language and Translation Technology TeamUniversity College GhentGhentBelgium
  2. 2.Ghent UniversityGhentBelgium

Personalised recommendations