An improved N-grams based Model for Authorship Attribution

Boughaci, Dalila; Benmesbah, Mounir; Zébiri, Aniss

doi:10.1109/iccisci.2019.8716391

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…One can determine the context and identify the writer of each text using these embeddings. Even with simpler embedders, embedding-based methods could be equally efficient [17].…”

Section: Related Workmentioning

confidence: 99%

Authorship Attribution using Sequential Part-of-Speech Pattern Mining

Alamanda¹,

Pabboju²,

Gugulothu³

2023

RIA

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Given an anonymous text, automatically attributing a name from a group of known writers is called "Authorship Attribution" (AA). It is a classification problem, and feature extraction techniques are initially applied, followed by the training of a model using a collection of texts whose authors are known. Numerous features, such as lexical, semantic, structural, n-grams, etc., can be used to identify the stylistic characteristics of writers. The authors of this research propose a novel approach to this problem by using sequential pattern mining on part-of-speech (PoS) tags. This paper introduces and discusses the concept of a Part-of-Speech Skip-Gram (PoSSG) that is different from traditional n-gram. A sequential pattern mining algorithm is applied to obtain PoSSG patterns, which are then used for authorship attribution tasks. Experimental studies on two different datasets: novels extracted from Project Gutenberg and Stamatatos06 Author Identification: C10-Attribution confirms that this approach of mining PoSSG patterns facilitates author identification.

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“…One can determine the context and identify the writer of each text using these embeddings. Even with simpler embedders, embedding-based methods could be equally efficient [17].…”

Section: Related Workmentioning

confidence: 99%

Authorship Attribution using Sequential Part-of-Speech Pattern Mining

Alamanda¹,

Pabboju²,

Gugulothu³

2023

RIA

View full text Add to dashboard Cite

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“…[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

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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.

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