A Unified Approach to Authorship Attribution and Verification

Puig, Xavier; Font, Martí; Ginebra, Josep

doi:10.1080/00031305.2016.1148630

Cited by 7 publications

(6 citation statements)

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“…• Stemming: to find the root of the words, we proposed to use the Khojah's stemmer. 6 To deal with the above preprocessing steps, we used the Alwajeeh's ArabicSF too 7 for both sub-corpora before extracting attribution features.…”

Section: ) Corpusmentioning

confidence: 99%

“…The early approaches had a statistical background [1]- [4] where the length and frequency of words, characteristics, and sentences were used to characterize the writing style. These approaches, in general, were human expert-based [5] and the applications also covered literary, religious and legal texts [6]. From the sixties of the last century up until the1990s, both the approaches and applications were shifted to cover new challenging problems such as The associate editor coordinating the review of this manuscript and approving it for publication was Fatih Emre Boran .…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

et al. 2020

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The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast-growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in the Arabic language, the AA problem has received less attention from the research community due to the complexity and nature of Arabic sentences. The paper presented an intensive review of previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, AdaBoost and Bagging ensemble methods have been applied to Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language. INDEX TERMS Authorship attribution, ensemble methods, stylometric features, TOPSIS method.

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Section: ) Corpusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

et al. 2020

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“…The frequency of use of function words is one of the best tools when it comes to discriminating styles, and that is what will be used here. Examples of the use of function words can be found in Mosteller and Wallace, Holmes, Zhao and Zobel, Giron et al, Riba and Ginebra, and Puig et al, among many other places.…”

Section: Outliers Among Acts Of Plays By Shakespearementioning

confidence: 99%

Outlier detection for multivariate categorical data

Puig

Ginebra

2018

Quality & Reliability Eng

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The detection of outlying rows in a contingency table is tackled from a Bayesian perspective, by adapting the framework adopted by Box and Tiao for normal models to multinomial models with random effects. The solution assumes a 2–component mixture model of 2 multinomial continuous mixtures for them, one for the nonoutlier rows and the second one for the outlier rows. The method starts by estimating the distributional characteristics of nonoutlier rows, and then it does cluster analysis to identify which rows belong to the outlier group and which do not. The method applies to any type of contingency table, and in particular, it could be used on the analysis of multivariate categorical control charts. Here, the use of the method is illustrated through a simulated example and by applying it to help identify heterogeneities of style among the acts in the plays of the First Folio edition of Shakespeare drama.

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“…In this settings, discriminant analysis should be used. A recent presentation of the Bayesian approach to this problem can be found in Puig, Font & Ginebra (2016).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of the Heterogeneity of Literary Style

Puig¹,

Font²,

Ginebra³

2016

Rev. colomb. estad.

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We proposed statistical analysis of the heterogeneity of literary style in a set of texts that simultaneously use different stylometric characteristics, like word length and the frequency of function words. The data set consists of several tables with the same number of rows, with the i-th row of all tables corresponding to the i-th text. The analysis proposed clusters the rows of all these tables simultaneously into groups with homogeneous style, based on a finite mixture of sets of multinomial models, one set for each table.Different from the usual heuristic cluster analysis approaches, our method naturally incorporates the text size, the discrete nature of the data, and the dependence between categories in the analysis. The model is checked and chosen with the help of posterior predictive checks, together with the use of closed form expressions for the posterior probabilities that each of the models considered to be appropriate. This is illustrated through an analysis of the heterogeneity in Shakespeare's plays, and by revisiting the authorshipattribution problem of Tirant lo Blanc.Key words: Authorship, Cluster analysis, Multinomial distribution. ResumenSe propone un análisis estadístico para modelar la heterogeneidad del estilo literario en un conjunto de textos, para ello se utilizan simultánea-mente diferentes características estilométricas, como longitud de palabra y la frecuencia de palabras función. Los datos consisten en varias tablas con el mismo número de filas, donde la fila i-ésima corresponde al texto i-ésimo. El análisis propuesto agrupa las filas de todas estas tablas simultáneamente en a Professor. E-mail: marti.font@upc.edu b Professor. E-mail: xavier.puig@upc.edu c Professor. E-mail: josep.ginebra@upc.edu 206Marti Font, Xavier Puig & Josep Ginebra grupos de estilo homogéneo, en base a una mezcla finita de modelos multinomiales.El modelo propuesto tiene la ventaja sobre los análisis de conglomerados heurísticos habituales, de incorporar de forma natural el tamaño del texto, la naturaleza discreta de los datos y la dependencia entre las categorías. El modelo se selecciona y válida con la ayuda de simulaciones de la distribución predictiva a posteriori, junto con el uso de las expresiones en forma cerrada para la probabilidad a posteriori de cada uno de los modelos de mezcla considerados. Todo ello se ilustra a través de un análisis de la heterogeneidad en las obras de Shakespeare, y revisitando el problema de atribución de autoría del texto Tirant lo Blanc.Palabras clave: análisi de conglomerados, atribución, distribución multinomial.

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A Unified Approach to Authorship Attribution and Verification

Cited by 7 publications

References 47 publications

Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

Outlier detection for multivariate categorical data

Bayesian Analysis of the Heterogeneity of Literary Style

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