most citedHeuristic Stopping Rules For Technology-Assisted Review

19 citations · 61 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20227 cited

TARexp: A Python Framework for Technology-Assisted Review Experiments

Eugene Yang, David D. Lewis

Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML). While a small TAR research community exists, the co…

cs.IR20214 cited

TAR on Social Media: A Framework for Online Content Moderation

Eugene Yang, David D. Lewis, Ophir Frieder

Content moderation (removing or limiting the distribution of posts based on their contents) is one tool social networks use to fight problems such as harassment and disinformation.…

cs.IR202115 cited

Certifying One-Phase Technology-Assisted Reviews

David D. Lewis, Eugene Yang, Ophir Frieder

Technology-assisted review (TAR) workflows based on iterative active learning are widely used in document review applications. Most stopping rules for one-phase TAR workflows lack…

cs.IR202119 cited

Heuristic Stopping Rules For Technology-Assisted Review

Eugene Yang, David D. Lewis, Ophir Frieder

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…

cs.IR202116 cited

On Minimizing Cost in Legal Document Review Workflows

Eugene Yang, David D. Lewis, Ophir Frieder

Technology-assisted review (TAR) refers to human-in-the-loop machine learning workflows for document review in legal discovery and other high recall review tasks. Attorneys and leg…