Combining Counting Processes and Classification Improves a Stopping Rule
  for Technology Assisted Review
Reem Bin-Hezam Mark Stevenson
Technology Assisted Review (TAR) stopping rules aim to reduce the cost of
manually assessing documents for relevance by minimising the number of
documents that need to be examined to ensure a desired level of recall. This
paper extends an effective stopping rule using information derived from a text
classifier that can be trained without the need for any additional annotation.
Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal
and RCV1) showed that the proposed approach consistently improves performance
and outperforms several alternative methods.