Probabilistic Anomaly Detection Method for Authorship Verification

Boukhaled, Mohamed Amine; Ganascia, Jean-Gabriel

doi:10.1007/978-3-319-11397-5_16

Cited by 6 publications

(12 citation statements)

References 15 publications

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“…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]. Especially for the first language detection, the effect of language transfer affects the use of function words in the secondary language [Torney et al 2012].…”

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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“…If D U falls inside this hypersphere, D U is accepted (U = A), otherwise rejected. In existing AV and AA works, OSVM served as a baseline (for example, [2,26]) or as the core method [33].…”

Section: One-class Support Vector Machine (Osvm)mentioning

confidence: 99%

Unary and Binary Classification Approaches and their Implications for Authorship Verification

Halvani¹,

Winter²,

Graner³

2019

Preprint

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Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to retrieve textual content of a certain author X, where the queried IR system is provided beforehand with a set of reference texts of X. Authorship verification (AV), which is a research subject in the field of digital text forensics, is suitable for this purpose. The task of AV is to determine if two documents (i.e. an indexed and a reference document) have been written by the same author X. Even though AV represents a unary classification problem, a number of existing approaches consider it as a binary classification task. However, the underlying classification model of an AV method has a number of serious implications regarding its prerequisites, evaluability, and applicability. In our comprehensive literature review, we observed several misunderstandings regarding the differentiation of unary and binary AV approaches that require consideration. The objective of this paper is, therefore, to clarify these by proposing clear criteria and new properties that aim to improve the characterization of existing and future AV approaches. Given both, we investigate the applicability of eleven existing unary and binary AV methods as well as four generic unary classification algorithms on two self-compiled corpora. Furthermore, we highlight an important issue concerning the evaluation of AV methods based on fixed decision criterions, which has not been paid attention in previous AV studies.

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“…Table 2 shows the distribution of PAN participants over the types of verification models as defined above. 6 It is clear that the majority of PAN participants follow an intrinsic, lazy, and instance-based methodology. Eager learning method began to be popular in late editions of PAN when the size of the training corpus allowed the development of relatively effective models [13].…”

Section: Verification Modelsmentioning

confidence: 99%

“…PAN evaluation campaigns provided benchmark corpora covering several natural languages and genres as well as an experimentation and evaluation framework to assess the performance of multiple verification methods. Since 2013, significant progress has been reported and multiple studies improved state-of-the-are methods, provided a better understanding of their strengths and weaknesses [6,7,9,18,22,30,45], and highlighted their applications in humanities and forensics [48,63,64]. This paper presents a review of recent advances in this field, focusing on the evaluation results of PAN shared tasks.…”

Section: Introductionmentioning

confidence: 99%

Authorship Verification: A Review of Recent Advances

Stamatatos¹

2016

RCS

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Authorship verification attempts to decide whether the author of a given set of texts is also the author of a disputed text. In comparison to closed-set and open-set attribution, the most popular tasks in relevant literature, the verification setting has some important advantages. First, it is more general since any attribution problem can be decomposed into a series of verification cases. Then, certain factors that affect the performance of closed-set and open-set attribution, like the candidate set size and the distribution of training texts over the candidate authors have limited impact on authorship verification. It is, therefore, more feasible to estimate the error rate of authorship attribution technology, needed in the framework of forensic applications, when focusing on the verification setting.Recently, there has been increasing interest for authorship verification, mainly due to the PAN shared tasks organized in 2013, 2014, and 2015. Multiple methods were developed and tested in new benchmark corpora covering several languages and genres. This paper presents a review of recent advances in this field focusing on the evaluation results of PAN shared tasks. Moreover, it discusses successes, failures, and open issues.

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Probabilistic Anomaly Detection Method for Authorship Verification

Cited by 6 publications

References 15 publications

Learning Stylometric Representations for Authorship Analysis

Learning Stylometric Representations for Authorship Analysis

Unary and Binary Classification Approaches and their Implications for Authorship Verification

Authorship Verification: A Review of Recent Advances

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