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Readability Formula for Russian Texts: A Modified Version

  • Marina SolnyshkinaEmail author
  • Vladimir Ivanov
  • Valery Solovyev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)

Abstract

The authors of the article offer new readability formulas for academic texts which provide a comparatively higher degree of accuracy than other Russian readability formulas. The results achieved are due to using original syntactic, lexical and frequency metrics ignored in previous research on Russian readability. The methods applied by the authors include Ridge and linear regression. The new readability formulas were computed on the Corpus of secondary school textbooks on Social Studies and then validated on the Corpus with the total size of 1 mln. tokens. The perspectives of the research lie in further modification of the formula for texts of various genres.

Keywords

Text readability formula Academic texts Russian language 

Notes

Acknowledgements

This research was financially supported by the Russian Science Foundation, grant #18-18-00436, the Russian Government Program of Competitive Growth of Kazan Federal University, and the subsidy for the state assignment in the sphere of scientific activity, grant agreement # 34.5517.2017/6.7. The Russian Academic Corpus (Sect. 3 in the paper) was created without supporting by the Russian Science Foundation.

References

  1. 1.
    Solnyshkina, M.I., Harkova, E.V., Kiselnikov, A.S.: Comparative Coh-Metrix analysis of reading comprehension texts: Unified (Russian) State Exam in English vs Cambridge First Certificate In English. English Lang. Teach. 7(12), 65–76 (2014)Google Scholar
  2. 2.
    Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32, 221–233 (1968)CrossRefGoogle Scholar
  3. 3.
    McLaughlin, G.: SMOG grading: a new readability formula. J. Reading 12(8), 639–646 (1969)Google Scholar
  4. 4.
    Nevdakh, M.M.: Research of information characteristics of educational text using methods of multidimensional statistical analysis. Appl. Inform. 4(16), 117–130 (2008)Google Scholar
  5. 5.
    Lorge, I.: Predicting readability. Teacher’s Coll. Rec. 45, 404–419 (1944)Google Scholar
  6. 6.
    Flesch, R.: Estimating the comprehension difficulty of magazine articles. J. Gen. Psychol. 28, 63–80 (1943)CrossRefGoogle Scholar
  7. 7.
    Matskovskii, M.S.: Problems of Readability of Printed Material. Semantic Perception of a Speech Message in Mass Communication, pp. 126–142. Nauka, Moscow (1976)Google Scholar
  8. 8.
    Oboroneva, I.V.: The automated estimation of complexity of educational texts on statistical parameters. Diss. Ped. n. M., 2006. 165 pGoogle Scholar
  9. 9.
    Falkenjack, J., Jonsson, A.: Classifying easy-to-read texts without parsing. In: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR) (2014)Google Scholar
  10. 10.
    Falkenjack, J., Heimann, M., Jönsson, A.: Features indicating readability in Swedish text. In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), pp. 27–40 (2013)Google Scholar
  11. 11.
    Piotrovsky, R.G. and others: Mathematical linguisticstick. Textbook. manual for ped. in-tov. M.: Higher School, 383 p. (1977)Google Scholar
  12. 12.
    Hultman, T.G., Westman, M.: Gymnasistsvenska. Liber, Lund (1977)Google Scholar
  13. 13.
    Cvrček, V., Chlumská, L.: Simplification in Translated Czech: A New Approach to Type-Token Ratio-Russian Linguistics, pp. 309–325. Springer, Dordrecht (2015).  https://doi.org/10.1007/s11185-015-9151-8CrossRefGoogle Scholar
  14. 14.
    Romanishin, G.V.: The study of the lexical wealth of scientific texts in New information technologies in automated systems: materials of the nineteenth scientific and practical seminar. M.: IPM them. M.V. Keldysh. - 352 p. (2016)Google Scholar
  15. 15.
    Karmanova, D.: Crisis of Russian higher education: towards the issue of aspectization labyrinth. J. Soc. Hum. Res. 1, 78–84 (2012)Google Scholar
  16. 16.
    Stepanov, V.I., Stepanova, O.T.: The crisis of education in Russia: the ways and causes of the exit. In: Non-State-Walled Education in Russia, Novosibirsk (1996)Google Scholar
  17. 17.
    Ivanov, V.V., Solnyshkina, M.I., Solovyev, V.D.: Efficiency of text readability features in Russian academic texts. Comput. Linguist. Intellect. Technol. 17, 277–287 (2018)Google Scholar
  18. 18.
    Reynolds, R.: Insights from Russian second language readability classification: complexity-dependent training requirements, and feature evaluation of multiple categories. In: Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 289–300 (2016)Google Scholar
  19. 19.
    Laposhina, A.N.: Analysis of relevant characteristics for automatic assessment of complexity of Russian texts used in courses for Russian as a foreign language [Electronic resource]: URL: http://www.dialog-21.ru/media/3993/laposhina.pdf. Accessed 10 July 2018
  20. 20.
    Sadov, M.A.: Development of an approach for measuring Russian text readability. Master course thesis. NRU HSE. 2018Google Scholar
  21. 21.
    Crossley, S., Allen, D., McNamara, D.: Text readability and intuitive simplification: a comparison of readability formulas. Read. Foreign Lang. 23(1), 84–101 (2011)Google Scholar
  22. 22.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge (2007)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marina Solnyshkina
    • 1
    Email author
  • Vladimir Ivanov
    • 2
  • Valery Solovyev
    • 1
  1. 1.Kazan Federal UniversityKazanRussia
  2. 2.Innopolis UniversityInnopolisRussia

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