19 citations · 67 across the 7 of their papers we have counts for
7 papers
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…
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.…
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…
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…
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…
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review
Eugene Yang, Sean MacAvaney, David D. Lewis +1
Technology-assisted review (TAR) refers to iterative active learning workflows for document review in high recall retrieval (HRR) tasks. TAR research and most commercial TAR softwa…
A Sequential Algorithm for Training Text Classifiers
David D. Lewis, William A. Gale
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which…