Open Set Authorship Attribution toward Demystifying Victorian Periodicals

Abstract

Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration. This restricted set-up is far from being realistic in dealing with highly entangled real-world AA tasks that involve a large number of candidate authors for attribution during test time. In this paper, we study AA in historical texts using a new data set compiled from the Victorian literature. We investigate the predictive capacity of most common English words in distinguishing writings of most prominent Victorian novelists. We challenged the closed-set classification assumption and discussed the limitations of standard machine learning techniques in dealing with the open set AA task. Our experiments suggest that a linear classifier can achieve near perfect attribution accuracy under closed set assumption yet, the need for more robust approaches becomes evident once a large candidate pool has to be considered in the open-set classification setting.

A review on authorship attribution in text mining

“…Nevertheless, limited research has been conducted on open-set attribution. Badirli et al (2019) discussed the limitations of using standard machine learning techniques for open-set authorship attribution problems. Their experiments suggest that linear classifiers can achieve near-perfect attribution accuracy under closed-set assumptions; however, a more robust approach is required once a large candidate pool is considered as in open-set classification.…”