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Technology Assisted Review (TAR), which aims to reduce the effort required toscreen collections of documents for relevance, is used to develop systematicreviews of medical evidence and identify documents that must be disclosed inresponse to legal proceedings. Stopping methods are algorithms which determinewhen to stop screening documents during the TAR process, helping to ensure thatworkload is minimised while still achieving a high level of recall. This paperproposes a novel stopping method based on point processes, which arestatistical models that can be used to represent the occurrence of randomevents. The approach uses rate functions to model the occurrence of relevantdocuments in the ranking and compares four candidates, including one that hasnot previously been used for this purpose (hyperbolic). Evaluation is carriedout using standard datasets (CLEF e-Health, TREC Total Recall, TREC Legal), andthis work is the first to explore stopping method robustness by reportingperformance on a range of rankings of varying effectiveness. Results show thatthe proposed method achieves the desired level of recall without requiring anexcessive number of documents to be examined in the majority of cases and alsocompares well against multiple alternative approaches.

Stopping Methods for Technology Assisted Reviews based on Point Processes