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<p>The practice of using statistical models in predicting authorship (so-called author-attribution models) is long established.  Several recent authorship attribution studies have indicated that topic-specific cues impact author-attribution machine learning  models. The arrival of new topics should be anticipated rather than ignored in an author attribution evaluation methodology; a  model that relies heavily on topic cues will be problematic in deployment settings where novel topics are common. In order to  effectively deal with novel topics, we create author and topic vectors and attempt to project out the topic influences from each  document. Although our experiments did not validate our assumptions, they do point out a possible problem with a common  assumption in authorship attribution research.
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<br>Subjects: Authorship; Computer science</div>
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Honaker, Randale J.</div>
Internet Archive identifier