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Statistical Models for Authorship Attribution

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Book cover Advances in Intelligent Systems and Computing IV (CSIT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1080))

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Abstract

A combination of three hypothesis methods has been proposed for authorship attribution. These are: the Student’s t-test, the Kolmogorov-Smirnov’s test and the chi-square test. This combination of methods can perfect adequacy of authorship attribution. With the help of these methods, the quantity of phoneme groups is minimized. The texts can be differentiated by one or three phoneme groups. The minimization of phoneme groups facilitates authorship attribution. On the basis of the results obtained, statistical models have been built. The models show author-differentiating capability of phoneme groups in authorship attribution. This way the highest author-differentiating capability of phoneme groups is determined. New software realizes the models applied. The program makes transforming a text into transcription faster. The transcription variants of words available in the program are used.

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References

  1. Argamon, S., Koppel, M., Pennebaker, J., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)

    Article  Google Scholar 

  2. Lytvyn, V.: Development of a method for the recognition of author’s style in the Ukrainian language texts based on linguometry, stylemetry and glottochronology. Eastern Eur. J. Enterp. Technol. 4/2(88), 10–18 (2017)

    Google Scholar 

  3. Mubin, S.T., Rajesh, S.P.: Authorship identification with multi sequence word selection method. In: Thermal Stresses—Advanced Theory and Applications, pp. 653–661 (2019).https://doi.org/10.1007/978-3-030-16657-1_61

  4. Jamak, A., Alen, S., Can, M.: Principal component analysis for authorship attribution. Bus. Syst. Res. 3(2), 49–56 (2012)

    Google Scholar 

  5. Zhao, Y., Zobel, J.: Searching with style: authorship attribution in classic literature. In: Proceedings of the Thirtieth Australasian Conference on Computer Science, Australian Computer Society, vol. 62, pp. 59–68 (2007)

    Google Scholar 

  6. Vysotska, V., Lytvyn, V., Hrendus, M., Kubinska, S., Brodyak, O.: Method of textual information authorship analysis based on stylometry. In: Proceedings of the XIII-th Scientific and Technical Conference, CSIT, Lviv, pp. 9–16 (2018)

    Google Scholar 

  7. Bisikalo, O.: Sentence syntactic analysis application to keywords identification Ukrainian texts. Radioelectronics Comput. Sci. Control 3(38), 54–65 (2016)

    Google Scholar 

  8. Altmann, G., Levickij, V., Perebyinis, V.: Problems of Quantitative Linguistics. Ruta, Chernivtsy (2005)

    Google Scholar 

  9. Ontika, N.N., Kabir, Md.F., Ashraful, I., Ahmed, E., Huda, M.N.: A computational approach to author identification from Bengali song lyrics. In: Proceedings of International Joint Conference on Computational Intelligence, pp. 359–369 (2019). https://doi.org/10.1007/978-981-13-7564-4_31

  10. Burrows, J.: Delta: a measure of stylistic difference and a guide to likely authorship. Literary Linguist. Comput. 17(3), 267–287 (2002)

    Article  Google Scholar 

  11. Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Computer-based authorship attribution without lexical measures. Comput. Humanit. 35/2, 193–214 (2001)

    Article  Google Scholar 

  12. Koppel, M.: Computational methods in authorship attribution. J. Assoc. Inf. Sci. Technol. 60(1), 9–26 (2009)

    Article  Google Scholar 

  13. Shulzinger, E., Bormashenko, E.: On the universal quantitative pattern of the distribution of initial characters in general dictionaries: the exponential distribution is valid for various languages. J. Quant. Linguist. 24, 273–288 (2017)

    Article  Google Scholar 

  14. Hausner, M.: Elementary Probability Theory. Springer, Boston (1995)

    Book  Google Scholar 

  15. Martindale, C.: On the utility of content analysis in author attribution: the federalist. Comput. Humanit. 29(4), 259–270 (1995)

    Article  Google Scholar 

  16. Waheed, A., Imran, S.B., Shabana, R.: Design and implementation of a machine learning-based authorship identification model. Sci. Program. 1–14 (2019). https://doi.org/10.1155/2019/9431073

  17. Ivanov, L., Aebig, A., Meerman, S.: Lexical stress-based authorship attribution with accurate pronunciation patterns selection. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) Text, Speech, and Dialogue, pp. 67–75. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00794-2_7

    Chapter  Google Scholar 

  18. Kolmogorov, A.N.: Foundations of the Theory of Probability. Chelsea Publishing (1950)

    Google Scholar 

  19. Kolmogorov, A.N.: Mathematics and its Historical Development. Nauka, Moscow (1991). Edited by V. A. Uspensky

    Google Scholar 

  20. Gnedenko, B.V., Kolmogorov, A.N.: Limit Distributions for Sums of Independent Variables. Addison-Wesley, Boston (1968)

    Google Scholar 

  21. Khomytska, I., Teslyuk, V.: Authorship and style attribution by statistical methods of style differentiation on the phonological level. In: Shakhovska, N. (ed.) Advances in Intelligent Systems and Computing III, Lviv, vol. 871, pp. 105–118 (2018)

    Google Scholar 

  22. Gomez, P.C.: Statistical Methods in Language and Linguistic Research. University of Murcia, Spain (2013)

    Google Scholar 

  23. Watanabe, S.: Probability Theory and Mathematical Statistics. Springer, Heidelberg (1988)

    Book  Google Scholar 

  24. Gries, Th.S.: Statistics for Linguistics with R: A Practical Introduction (Trends in Linguistics: Studies & Monographs), p. 348 (2009)

    Google Scholar 

  25. Kornai, A.: A Mathematical Linguistics. Springer, London (2008). https://doi.org/10.1007/978-1-84628-986-6

    Book  MATH  Google Scholar 

  26. Khomytska, I., Teslyuk, V., Holovatyy, A., Morushko, O.: Development of methods, models and means for the author attribution of a text. Eastern Eur. J. Enterp. Technol. 3/2(93), 41–46 (2018)

    Google Scholar 

  27. Rozanov, I.A., Silverman, R.A.: Probability Theory: A Concise Course. Dover Publications Inc., New York (2007)

    Google Scholar 

  28. Jorgensen, P.E.T.: Analysis and Probability. Springer, New York (2006)

    MATH  Google Scholar 

  29. Bhattacharya, R., Waymire, E.C.: A Basic Course in Probability Theory, 2nd edn. Springer, Cham (2017)

    MATH  Google Scholar 

  30. Khomytska, I., Teslyuk, V., Labinska, L.: Program system of authorship attribution of texts on the phonological level. In: Proceedings of the XIV-th Scientific-Practical Conference “Problems and Perspectives of Development of Economics, Enterprise and Computer Technologies in Ukraine”, pp. 15–16. LPNU, Lviv (2018)

    Google Scholar 

  31. Bobalo, Yu., Seniv, M., Yakovyna, V., Symets, I.: Method of reliability block diagram visualization and automated construction of technical system operability condition. In: Shakhovska, N. (ed.) Advances in Intelligent Systems and Computing III, vol. 871, pp. 599–610 (2019)

    Google Scholar 

  32. Batyuk, A., Voityshyn, V., Verhun, V.: Software architecture design of the real-time processes monitoring platform. In: IEEE Second International Conference on Data Stream Mining & Processing (DSMP 2018), Lviv, Ukraine, pp. 98–101 (2018)

    Google Scholar 

  33. Shakhovska, N., Vysotska, V., Chyrun, L.: Intelligent systems design of distance learning realization for modern youth promotion and involvement in independent scientific researches. In: Shakhovska, N. (ed.) Advances in Intelligent Systems and Computing, vol. 512, pp. 175–198 (2016)

    Google Scholar 

  34. Denysyuk, P.: Usage of XML for fluidic MEMS database design. In: International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH 2006), Lviv-Polyana, p. 148 (2007)

    Google Scholar 

  35. Teorey, T.J., Lightstone, S.S., et al.: Database Design: Know it all, 1st edn. Morgan Kaufmann Publishers, Burlington (2009)

    Google Scholar 

  36. Horstmann, C.: Java SE 9. Core Java SE 9 for the Impatient. “Bильямc” (2018)

    Google Scholar 

  37. Khomytska, I., Teslyuk, V.: Mathematical methods applied for authorship attribution on the phonological level. In: Proceedings of International Scientific Conference Computer Sciences and Information Technologies (CSIT-2019), vol. 3, pp. 7–11 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

    Iryna Khomytska & Vasyl Teslyuk

Authors
  1. Iryna Khomytska
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  2. Vasyl Teslyuk
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Correspondence to Vasyl Teslyuk .

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Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Khomytska, I., Teslyuk, V. (2020). Statistical Models for Authorship Attribution. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_39

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