  Text retrieval systems often return large sets of documents, particularly
when applied to large collections. Stopping criteria can reduce the number of
these documents that need to be manually evaluated for relevance by predicting
when a suitable level of recall has been achieved. In this work, a novel method
for determining a stopping criterion is proposed that models the rate at which
relevant documents occur using a Poisson process. This method allows a user to
specify both a minimum desired level of recall to achieve and a desired
probability of having achieved it. We evaluate our method on a public dataset
and compare it with previous techniques for determining stopping criteria.
