Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.186834

Keywords:

NLP, content, content-monitoring, stop-words, content-analysis, statistical linguistic analysis, quantitative linguistics, statistical linguistics, linguometry

Abstract

The peculiarities of the application of linguo-statistics technologies for the identification of the style of the author of text content of scientific and technical profile are considered. Quantitative linguistic analysis of a text uses the benefits of content monitoring based on the NLP methods to identify and analyze the set of stop words, keywords, set phrases and to study N-gram. The latter are used in the linguometry methods to determine in per cent if the given text belongs to a particular author. The quantitative method for automatic text content authorship attribution was developed based on statistical analysis of the 3-gram distribution. The approach to the implementation of identification of the author of the text in the Ukrainian language of the scientific and technical profile was proposed. Experimental results of the proposed method to determine the belonging of the analyzed text to a specific author in the presence of the reference text were obtained. Application of the linguo-statistical analysis of the 3-grams to a set of articles will make it possible to form a subset of publications that are similar in linguistic descriptions. Imposing additional conditions in the form of statistical and quantitative analyses (a set of keywords, set expressions, stylometric, linguometric analyses, etc.) on a subset will allow a significant reduction of this subset by specifying the list of the most likely author. For qualitative and effective content analysis when determining the degree of authorship of a particular author, we propose to analyze the reference text and the one under consideration at several stages: linguometric analysis of the coefficients of the diversity of the author's speech, stylometric analysis, analysis of set expressions, linguo-statistical analysis of 3-grams. For automated text processing, not only the frequency of occurrence of a certain category, but also its existence in the studied text in general are important. Quantitative computation makes it possible to draw objective conclusions about the orientation of materials by the number of using the units of analysis in the studied texts. Qualitative analysis does the same, but as a result of the study of whether (and in what context) there is a certain important original category in general

Author Biographies

Vasyl Lytvyn, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

Doctor of Technical Sciences, Professor

Department of Information Systems and Networks

Victoria Vysotska, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

PhD, Associate Professor

Department of Information Systems and Networks

Ihor Budz, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

PhD, Associate Professor

Department of Computational Mathematics and Programming

Yaroslav Pelekh, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

PhD, Associate Professor

Department of Computational Mathematics and Programming

Nataliia Sokulska, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79026

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Roman Kovalchuk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79026

PhD, Associate Professor

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Lyudmyla Dzyubyk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79026

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Oksana Tereshchuk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79026

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Myroslav Komar, Ternopil National Economic University Lvivska str., 11, Ternopil, Ukraine, 46009

PhD

Department of Information and Computing Systems and Control

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2019-12-17

How to Cite

Lytvyn, V., Vysotska, V., Budz, I., Pelekh, Y., Sokulska, N., Kovalchuk, R., Dzyubyk, L., Tereshchuk, O., & Komar, M. (2019). Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution. Eastern-European Journal of Enterprise Technologies, 6(2 (102), 28–51. https://doi.org/10.15587/1729-4061.2019.186834

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Vol. 6 No. 2 (102) (2019): Information technology. Industry control systems

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Information technology

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Copyright (c) 2019 Vasyl Lytvyn, Victoria Vysotska, Ihor Budz, Yaroslav Pelekh, Nataliia Sokulska, Roman Kovalchuk, Lyudmyla Dzyubyk, Oksana Tereshchuk, Myroslav Komar

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