Development of Science and Society in the Digital Economy, Petrozavodsk ICNP "New Science" (English text pp. 154-200).
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: We present some results obtained in the framework of the project “The novel method of text attribution based on the numerals statistics” supported by a grant from the Russian Foundation for Basic Research, project No. 19-012-00199A. We suggest two approaches to the statistical analysis of texts, both based on the study of numerals occurrence in texts. The 1st approach is related to the study of the frequency distribution of various leading digits of numerals occurring in the text. These frequencies are unequal: the digit 1 is strongly dominating; usually, the incidence of subsequent digits is monotonically decreasing. The frequencies of occurrence of the digit 1, as well as, to a lesser extent, the digits 2 and 3, are usually a characteristic author's style feature, manifested in all (sufficiently long) literary texts of any author. This approach is convenient for testing whether a group of texts has common authorship: the latter is dubious if the frequency distributions are sufficiently different. The 2nd approach is the extension of the first one and requires the study of the frequency distribution of numerals themselves (not their leading digits). The approach yields non-trivial information about the author, stylistic and genre peculiarities of the texts and is suited for the advanced stylometric analysis. The proposed approaches are illustrated by examples of computer analysis of the literary texts in Russian, Czech, Lithuanian, English, and Turkish.
Bibtex:
@article{,
author = {Zenkov, Andrei Viacheslavovich and Zenkov, Eugene Viacheslavovich and Zenkov, Miroslav Andreevich},
title = {The Novel Method of Text Attribution Based on the Numerals Statistics: A Survey of Results},
year = {2021},
journal = {Development of Science and Society in the Digital Economy, Petrozavodsk ICNP "New Science"},
pages = {154--200},
url = {https://m.sciencen.org/assets/Kontent/Monografii-2/Arhiv-monografij/MON-79.pdf#page=137},
}
Reference Type: Journal Article
Subject Area(s): Statistics