Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
This addresses efficiency challenges for reviewers in legal or e-discovery domains, though it is incremental as it builds on existing active learning methods.
The study tackled the problem of reducing time and effort in high-recall information retrieval by evaluating sentence-level relevance feedback versus full documents, finding that using isolated sentences yields comparable accuracy and higher efficiency compared to a state-of-the-art baseline.
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.