Skip to main content
SpringerLink
Log in

Single-Sentence Readability Prediction in Russian

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 436))

Abstract

In an effort to make reading more accessible, an automated readability formula can help students to retrieve appropriate material for their language level. This study attempts to discover and analyze a set of possible features that can be used for single-sentence readability prediction in Russian. We test the influence of syntactic features on predictability of structural complexity. The readability of sentences from SynTagRus corpus was marked up manually and used for evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://texts.cie.ru

References

  1. Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32, 221 (1948)

    Article  Google Scholar 

  2. Kincaid, J.P., Fishburne, Jr., R.P., Rogers, R.L., Chissom, B.S.: Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. DTIC Document (1975)

    Google Scholar 

  3. Chall, J.S.: Readability Revisited: The New Dale-Chall Readability Formula. Brookline Books Cambridge, MA (1995)

    Google Scholar 

  4. Collins-Thompson, K., Callan, J.: Predicting reading difficulty with statistical language models. J. Am. Soc. Inf. Sci. Technol. 56, 1448–1462 (2005)

    Article  Google Scholar 

  5. Schwarm, S.E., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 523–530. Association for Computational Linguistics (2005)

    Google Scholar 

  6. Oborneva, I.: Automatic assessment of the complexity of educational texts on the basis of statistical parameters (2006)

    Google Scholar 

  7. Krioni, N., Nikin, A., Filippova, A.: Automated system for analysis of the complexity of educational texts. Manag. Soc. Econ. Syst. 11, 101–107 (2008)

    Google Scholar 

  8. Verhelst, N., Van Avermaet, P., Takala, S., Figueras, N., North, B.: Common European Framework of Reference for Languages: Learning, Teaching. Assessment. Cambridge University Press, Cambridge (2009)

    Google Scholar 

  9. Bocharov, V., Stepanova, M., Ostapuk, N., Bichineva, S., Granovsky, D.: Quality assurance tools in the OpenCorpora project. In: Computational Linguistics and Intelligent Technology: Proceeding of the International Conference « Dialog–2011 » , pp. 10–17 (2011)

    Google Scholar 

  10. Francois, T.L.: Combining a statistical language model with logistic regression to predict the lexical and syntactic difficulty of texts for FFL. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pp. 19–27. Association for Computational Linguistics (2009)

    Google Scholar 

  11. Nevdah, M.: Development of a method of automated evaluation of the complexity of educational texts for higher school (2008)

    Google Scholar 

  12. Kent, J.T.: Information gain and a general measure of correlation. Biometrika 70, 163–173 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  13. Nivre, J., Boguslavsky, I.M., Iomdin, L.L.: Parsing the SynTagRus treebank of Russian. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 641–648. Association for Computational Linguistics (2008)

    Google Scholar 

Download references

Acknowledgment

This study comprises research findings from the «Adaptation of texts from the Russian National Corpus» for the electronic textbook «Russian language as a foreign one» carried out within The National Research University Higher School of Economics’ Academic Fund Program in 2013, grant No 13-05-0031.

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Nikolay Karpov, Julia Baranova & Fedor Vitugin

Authors
  1. Nikolay Karpov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julia Baranova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fedor Vitugin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikolay Karpov .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovsky Inst. of Math. and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Université catholique de Louvain, Moscow, Russia

    Alexander Panchenko

  4. University of Wolverhampton, Wolverhampton, United Kingdom

    Natalia Konstantinova

  5. National Research University, Moscow, Russia

    Rostislav E. Yavorsky

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Karpov, N., Baranova, J., Vitugin, F. (2014). Single-Sentence Readability Prediction in Russian. In: Ignatov, D., Khachay, M., Panchenko, A., Konstantinova, N., Yavorsky, R. (eds) Analysis of Images, Social Networks and Texts. AIST 2014. Communications in Computer and Information Science, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-319-12580-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12580-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12579-4

  • Online ISBN: 978-3-319-12580-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation