Novel topic authorship attribution
Honaker, Randale J.
MetadataShow full item record
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.
Approved for public release; distribution is unlimited
Showing items related by title, author, creator and subject.
Caver, Johnnie F. (Monterey, California. Naval Postgraduate School, 2009-12);Several authorship attribution studies have speculated about the existence of a link between topic cues and author style features. This research presents a novel experimental protocol for measuring the impact of topic ...
Redmond, Matthew G.; Busbey, Noah E. B. (Monterey, California: Naval Postgraduate School, 2017-12);The types of attribution for influence activities span a spectrum that includes true attribution, non-attribution, concurring partner attribution, and false attribution. The U.S. Department of Defense sits in a unique ...
Use of Attribution and Forensic Science in Addressing Biological Weapon Threats: A Multi-Faceted Study Bidwell, Christopher A.; Bhatt, Kishan (Federation of American Scientists, 2016-02);The threat from the manufacture, proliferation, and use of biological weapons (BW) is a high priority concern for the U.S. Government. As reflected in U.S. Government policy statements and budget allocations, deterrence ...