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We adapt the Higher Criticism (HC) goodness-of-fit test to measure thecloseness between word-frequency tables. We apply this measure to authorshipattribution challenges, where the goal is to identify the author of a documentusing other documents whose authorship is known. The method is simple yetperforms well without handcrafting and tuning; reporting accuracy at the stateof the art level in various current challenges. As an inherent side effect, theHC calculation identifies a subset of discriminating words. In practice, theidentified words have low variance across documents belonging to a corpus ofhomogeneous authorship. We conclude that in comparing the similarity of a newdocument and a corpus of a single author, HC is mostly affected by wordscharacteristic of the author and is relatively unaffected by topic structure.

Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship