Approach for Minimization of Phoneme Groups in Authorship Attribution

Abstract

The developed mathematical support for authorship attribution software includes a combination of statistical methods (Student’s t-test, Kolmogorov-Smirnov’s test) and a statistical model for determining significant differences between styles. The combination of statistical methods allows us to enhance test validity of authorship attribution by obtaining the same results by the two methods applied. The model developed makes it possible to identify a consonant phoneme group with high style identification capability. The phoneme position in a word is taken into account. The greater number of significant differences is, the higher authorship identification capability of the phoneme group is. The developed system software is based on the algorithms of the used combination of methods and statistical model. The Java programming language provides platform independence. The minimized number of consonant phoneme groups makes the process of style and authorship attribution more automated. The obtained results of comparisons of the scientific, belles-lettres, conversational and newspaper styles are presented. The data obtained allows us to assert that the used combination of methods and the developed statistical model improve test validity of style and authorship attribution.

A review on authorship attribution in text mining

“…The reliability of phonemes comes from the fact that there are specific rules about the relationship between phonemes and syllables, and syllables have been used in authorship attribution since the study by Mosteler and Wallace (2008). According to Khomytska et al (2020), the authorship attribution of a text at the phonological level can be performed according to three criteria of differentiation (absolute frequency of phoneme groups; relative frequency of phoneme groups; and average frequency of phoneme groups) and three positions of phonemes in a word (arbitrary position in a word; at the beginning of a word; and at the end of a word). Furthermore, by studying 16 native‐English authors, Deng and Allahverdyan (2016) showed that rank‐frequency relations for phonemes can be described by the Dirichlet distribution and demonstrated that these relations without the frequencies of specific phonemes are author dependent.…”