Writer identification based on letter frequency distribution

Diurdeva, Polina; Mikhailova, Elena; Shalymov, Dmitry S.

doi:10.23919/fruct.2016.7892179

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[7][8][9], Дюрдева та iн. [10], Peng et al [11], Keselj et al [12], Boughaci [13], Ярошевський та Клюшин [14] та багато iнших дослiдникiв. В цих роботах розглянутi рiзнi подходи до автоматичної iдентификацiї авторства i проведенi численнi тести для оцiнки точностi i ефективностi запропонованих методiв для атрибуцiї лiтературних текстiв, написаних рiзними мовами.…”

Section: вступunclassified

Nonparametric Methods of Authorship Attribution in English Literature

Klyushin

Mykhaylyuk

2020

JNAM

View full text Add to dashboard Cite

The paper describes the results of comparison of two nonparametric methods of authorship identification in English literature. It describes testing methods with and without clustering. A method was also proposed to select the n-grams that would best serve as a marker to identify the author. More than 800 texts of 16 authors were used for testing. The method using the density of the distribution is suitable for identifying authors of both large texts (50000+ characters) and small (10000+ characters) ones. A method that uses p-statistics is only suitable for large texts.

show abstract

Section: вступunclassified

Nonparametric Methods of Authorship Attribution in English Literature

Klyushin

Mykhaylyuk

2020

JNAM

View full text Add to dashboard Cite

show abstract

“…This paper is a revised and extended version of the research of Diurdeva, Mikhailova, and Shalymov(2016).Intherestofthispaper,section2givesanoverviewofrelatedwork.Section3-4 describesthemethodologyanddatasets.Section5-6presentsanexperimentalevaluation.Finally, section7drawstheconclusions.…”

Section: Introductionmentioning

confidence: 99%

N-Gram Based Approach for Text Authorship Classification

Mikhailova

Diurdeva

Shalymov

2017

International Journal of Embedded and Real-Time Communication Systems

Self Cite

View full text Add to dashboard Cite

Automated authorship attribution is actual to identify the author of an anonymous texts, or texts whose authorship is in doubt. It can be used in various applications including author verification, plagiarism detection, computer forensics and others. In this article, the authors analyze an approach based on frequency combination of letters is investigated for solving such a task as classification of documents by authorship. This technique could be used to identify the author of a computer program from a predefined set of possible authors. The effectiveness of this approach is significantly determined by the choice of metric. The research examines and compares four different distance measures between a text of unknown authorship and an authors' profile: L1 measure, Kullback-Leibler divergence, base metric of Common N-gram method (CNG) and a certain variation of dissimilarity measure of CNG method. Comparison outlines cases when some metric outperforms others with a specific parameter combination. Experiments are conducted on different Russian and English corpora.

show abstract

Nonparametric Methods of Authorship Attribution in Ukrainian Literature

Klyushin

Nykyporets²

2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Writer identification based on letter frequency distribution

Cited by 4 publications

References 8 publications

Nonparametric Methods of Authorship Attribution in English Literature

Nonparametric Methods of Authorship Attribution in English Literature

N-Gram Based Approach for Text Authorship Classification

Nonparametric Methods of Authorship Attribution in Ukrainian Literature

Contact Info

Product

Resources

About