Influence of Data Discretization on Efficiency of Bayesian Classifier for Authorship Attribution

Baron, Grzegorz

doi:10.1016/j.procs.2014.08.201

Cited by 20 publications

(8 citation statements)

References 19 publications

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“…They have been shown to be effective for capturing the differences in writing styles [Koppel et al 2002]. Usually the frequency value of the function words are used to represent the features [Baron 2014;Halvani and Steinebach 2014;HaCohen-Kerner and Margaliot 2014]. They are effective for identifying the first language of the authors [Torney et al 2012;Argamon et al 2009], identifying the actual author of French literature [Boukhaled and Ganascia 2014], and characterizing the gender of e-mails [Corney et al 2002].…”

Section: Joint Learning Model For Topical Modality and Lexical Modalitymentioning

confidence: 99%

“…Syntactic features are usually considered as deep linguistic features that are comparatively more difficult to consciously manipulate [Gamon 2004]. Typically, the Part-of-Speech (POS) tags n-grams are used as the features for this modality [Boukhaled and Ganascia 2014;Baron 2014;Qian et al 2014]. Given a token t b , its Part-of-Speech (POS) tag represents its grammatical role in the sentence.…”

Section: The Syntactic Modalitymentioning

confidence: 99%

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Learning Stylometric Representations for Authorship Analysis

Ding

Fung

Iqbal

et al. 2019

IEEE Trans. Cybern.

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Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario-or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations.

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Section: Joint Learning Model For Topical Modality and Lexical Modalitymentioning

confidence: 99%

Section: The Syntactic Modalitymentioning

confidence: 99%

Learning Stylometric Representations for Authorship Analysis

Ding

Fung

Iqbal

et al. 2019

IEEE Trans. Cybern.

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“…Two main circumstances can be mentioned, where discretization may or even must be applied. The first situation is when there are some suspicions about possible improvement of a decision system quality when discretized data is applied [2]. The second one is when method or algorithm employed in decision system can operate only on nominal, discrete data.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

Baron

2016

Communications in Computer and Information Science

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Abstract. The presented paper addresses problem of evaluation of decision systems in authorship attribution domain. Two typical approaches are cross-validation and evaluation based on specially created test datasets. Sometimes preparation of test sets can be troublesome. Another problem appears when discretization of input sets is taken into account. It is not obvious how to discretize test datasets. Therefore model evaluation method not requiring test sets would be useful. Cross-validation is the well-known and broadly accepted method, so the question arose if it can deliver reliable information about quality of prepared decision system. The set of classifiers was selected and different discretization algorithms were applied to obtain method invariant outcomes. The comparative results of experiments performed using cross-validation and test sets approaches to system evaluation, and conclusions are presented.

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“…The main idea behind AISD is to identify the true author of a disputed document from a set of candidate authors. Existing studies of AISD have reported good results [17,3]. However, as already explained in the introduction section, existing authorship identification techniques designed to handle single-author documents are inapplicable to multi-author documents [5].…”

Section: Authorship Identificationmentioning

confidence: 99%

A Scalable Framework for Stylometric Analysis of Multi-author Documents

Sarwar

Nutanong

et al. 2018

Database Systems for Advanced Applications

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Stylometry is a statistical technique used to analyze the variations in the author's writing styles and is typically applied to authorship attribution problems. In this investigation, we apply stylometry to authorship identification of multi-author documents (AIMD) task. We propose an AIMD technique called Co-Authorship Graph (CAG) which can be used to collaboratively attribute different portions of documents to different authors belonging to the same community. Based on CAG, we propose a novel AIMD solution which (i) significantly outperforms the existing state-of-the-art solution; (ii) can effectively handle a larger number of co-authors; and (iii) is capable of handling the case when some of the listed co-authors have not contributed to the document as a writer. We conducted an extensive experimental study to compare the proposed solution and the best existing AIMD method using real and synthetic datasets. We show that the proposed solution significantly outperforms existing state-of-the-art method.

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Influence of Data Discretization on Efficiency of Bayesian Classifier for Authorship Attribution

Cited by 20 publications

References 19 publications

Learning Stylometric Representations for Authorship Analysis

Learning Stylometric Representations for Authorship Analysis

Comparison of Cross-Validation and Test Sets Approaches to Evaluation of Classifiers in Authorship Attribution Domain

A Scalable Framework for Stylometric Analysis of Multi-author Documents

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