SAL τ : efficiently stopping TAR by improving priors estimates

In high recall retrieval tasks, human experts review a large pool of documents with the goal of satisfying an information need. Documents are prioritized for review through an active learning policy, and the process is usually referred to as Technology-Assisted Review (TAR). TAR tasks also aim to stop the review process once the target recall is achieved to minimize the annotation cost. In this paper, we introduce a new stopping rule called SAL _τ ^R (SLD for Active Learning), a modified version of the Saerens–Latinne–Decaestecker algorithm (SLD) that has been adapted for use in active learning. Experiments show that our algorithm stops the review well ahead of the current state-of-the-art methods, while providing the same guarantees of achieving the target recall.