Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship
Abstract
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.
Cite
@article{arxiv.1911.01208,
title = {Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship},
author = {Alon Kipnis},
journal= {arXiv preprint arXiv:1911.01208},
year = {2023}
}