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20172026
most citedExpansion via Prediction of Importance with Contextualization

72 citations · 275 across the 25 of their papers we have counts for

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Showing 2021 · cs.IRShow all

5 papers · 2 filters

cs.IR2021★ 4 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.IR2021★ 15 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.IR2021★ 19 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.IR2021★ 16 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…

cs.IR2021

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…