Stochastic Model for Phonemes Uncovers an Author-Dependency of Their Usage

Deng, Weibing; Allahverdyan, Armen E.

doi:10.1371/journal.pone.0152561

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…because the phonemes can participate in the meaning-formation, as witnessed in sound symbolism [32]. Moreover, phonemes participate in text-formation not only via words, but also directly [33].…”

Section: Summary and Discussionmentioning

confidence: 99%

Optimal alphabet for single text compression

Allahverdyan¹,

Khachatryan²

2022

Preprint

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A text can be viewed via different representations, i.e. as a sequence of letters, n-grams of letters, syllables, words, and phrases. Here we study the optimal noiseless compression of texts using the Huffman code, where the alphabet of encoding coincides with one of those representations. We show that it is necessary to account for the codebook when compressing a single text. Hence, the total compression comprises of the optimally compressed text -characterized by the entropy of the alphabet elements, and the codebook which is text-specific and therefore has to be included for noiseless (de)compression. For texts of Project Gutenberg the best compression is provided by syllables, i.e. the minimal meaning-expressing element of the language. If only sufficiently short texts are retained, the optimal alphabet is that of letters or 2-grams of letters depending on the retained length.

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Section: Summary and Discussionmentioning

confidence: 99%

Optimal alphabet for single text compression

Allahverdyan¹,

Khachatryan²

2022

Preprint

Self Cite

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“…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. Khomytska et al (2018) conducted experiments on eight groups of consonant phonemes (labial, front-alveolar, mid-alveolar, post-alveolar, nasal, sonorous, slit, and closed) in English texts related to fiction, conversational, newspaper, and scientific styles.…”

Section: Phoneme N-gram Featuresmentioning

confidence: 99%

A review on authorship attribution in text mining

Zheng

Jin

2022

WIREs Computational Stats

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The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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“…The Student's t-test and the chi-square test are less powerful than the Kolmogorov-Smirnov's test [12]. The rankfrequency relations for phonemes of a text depend on its author and can be described by the Dirichlet distribution [14]. The ranking method is less powerful than the hypothesis method [15].…”

Section: Related Workmentioning

confidence: 99%

Approach for Minimization of Phoneme Groups in Authorship Attribution

Khomytska¹,

Teslyuk²,

Bazylevych³

et al. 2020

IJC

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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.

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Stochastic Model for Phonemes Uncovers an Author-Dependency of Their Usage

Cited by 5 publications

References 35 publications

Optimal alphabet for single text compression

Optimal alphabet for single text compression

A review on authorship attribution in text mining

Approach for Minimization of Phoneme Groups in Authorship Attribution

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