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Retrieving indexed documents, not by their topical content but their writingstyle 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 referencetexts of X. Authorship verification (AV), which is a research subject in thefield of digital text forensics, is suitable for this purpose. The task of AVis 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 unaryclassification problem, a number of existing approaches consider it as a binaryclassification task. However, the underlying classification model of an AVmethod has a number of serious implications regarding its prerequisites,evaluability, and applicability. In our comprehensive literature review, weobserved several misunderstandings regarding the differentiation of unary andbinary AV approaches that require consideration. The objective of this paperis, therefore, to clarify these by proposing clear criteria and new propertiesthat aim to improve the characterization of existing and future AV approaches.Given both, we investigate the applicability of eleven existing unary andbinary AV methods as well as four generic unary classification algorithms ontwo self-compiled corpora. Furthermore, we highlight an important issueconcerning the evaluation of AV methods based on fixed decision criterions,which has not been paid attention in previous AV studies.

Unary and Binary Classification Approaches and their Implications for Authorship Verification