  This study uses a novel simulation framework to evaluate whether the time and
effort necessary to achieve high recall using active learning is reduced by
presenting the reviewer with isolated sentences, as opposed to full documents,
for relevance feedback. Under the weak assumption that more time and effort is
required to review an entire document than a single sentence, simulation
results indicate that the use of isolated sentences for relevance feedback can
yield comparable accuracy and higher efficiency, relative to the
state-of-the-art Baseline Model Implementation (BMI) of the AutoTAR Continuous
Active Learning ("CAL") method employed in the TREC 2015 and 2016 Total Recall
Track.
