  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 anew 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.
