  We adapt the Higher Criticism (HC) goodness-of-fit test to measure the
closeness between word-frequency tables. We apply this measure to authorship
attribution challenges, where the goal is to identify the author of a document
using other documents whose authorship is known. The method is simple yet
performs well without handcrafting and tuning; reporting accuracy at the state
of the art level in various current challenges. As an inherent side effect, the
HC calculation identifies a subset of discriminating words. In practice, the
identified words have low variance across documents belonging to a corpus of
homogeneous authorship. We conclude that in comparing the similarity of a new
document and a corpus of a single author, HC is mostly affected by words
characteristic of the author and is relatively unaffected by topic structure.

Computation and Language;Machine Learning;Computation;Machine Learning