Unary and Binary Classification Approaches and their Implications for Authorship Verification

Abstract

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.

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

“…In the past two decades, researchers from different disciplines, including linguistics, psychology, computer science, and mathematics, proposed a range of techniques and concepts for this task [181][182][183][184]. Stylometric learning is a computational approach for many linguistic tasks, such as authorship verification [185].…”

Datasets and Models for Authorship Attribution on Italian Personal Writings

“…As a general observation, even in later challenges, SVMs have proven to be the most effective for AA tasks (Kestemont et al, 2019). More specifically, in a survey of freely available AA systems, GLAD showed best performance and especially high adaptability to new datasets (Halvani et al, 2018). Lastly, de Vries (2020) has explored fine-tuning a pre-trained model for AV in Dutch, a less-resourced language compared to English.…”

“…The GLAD system of Hürlimann et al (2015) was specifically developed to solve AV problems, and has been shown to be highly adaptable to new datasets (Halvani et al, 2018). GLAD uses an SVM with a variety of features including character level ones, which have proved to be most effective for AA tasks (Stamatatos, 2009;Moreau et al, 2015;Hürlimann et al, 2015), and is freely available.…”