Typical authorship attribution methods are based on the assumption that we have a small closed set of candidate authors. In law enforcement scenarios, this assumption is often violated. There might be no closed set of suspects at all or there might be a closed set containing thousands of suspects. We show how even under such circumstances, we can make useful claims about authorship.
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