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arXiv · 2106.09871

Heuristic Stopping Rules For Technology-Assisted Review

Eugene Yang·David D. Lewis·Ophir Frieder
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Abstract

Technology-assisted review (TAR) refers to human-in-the-loop active learning workflows for finding relevant documents in large collections. These workflows often must meet a target for the proportion of relevant documents found (i.e. recall) while also holding down costs. A variety of heuristic stopping rules have been suggested for striking this tradeoff in particular settings, but none have been tested against a range of recall targets and tasks. We propose two new heuristic stopping rules, Quant and QuantCI based on model-based estimation techniques from survey research. We compare them against a range of proposed heuristics and find they are accurate at hitting a range of recall targets while substantially reducing review costs.

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Eugene Yang, David D. Lewis, Ophir Frieder. 2021-06-18. Heuristic Stopping Rules For Technology-Assisted Review. https://doi.org/10.1145/3469096.3469873

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