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The goal of a technology-assisted review is to achieve high recall with lowhuman effort. Continuous active learning algorithms have demonstrated goodperformance in locating the majority of relevant documents in a collection,however their performance is reaching a plateau when 80\%-90\% of them has beenfound. Finding the last few relevant documents typically requires exhaustivelyreviewing the collection. In this paper, we propose a novel method to identifythese last few, but significant, documents efficiently. Our method makes thehypothesis that entities carry vital information in documents, and thatreviewers can answer questions about the presence or absence of an entity inthe missing relevance documents. Based on this we devise a sequential Bayesiansearch method that selects the optimal sequence of questions to ask. Theexperimental results show that our proposed method can greatly improveperformance requiring less reviewing effort.

Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers