Search Results for author: David D. Lewis

Found 7 papers, 4 papers with code

TARexp: A Python Framework for Technology-Assisted Review Experiments

1 code implementation23 Feb 2022 Eugene Yang, David D. Lewis

Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML).

Retrieval TAR

TAR on Social Media: A Framework for Online Content Moderation

1 code implementation29 Aug 2021 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.

Active Learning Retrieval +1

Certifying One-Phase Technology-Assisted Reviews

no code implementations29 Aug 2021 David D. Lewis, Eugene Yang, Ophir Frieder

Technology-assisted review (TAR) workflows based on iterative active learning are widely used in document review applications.

Active Learning TAR +1

On Minimizing Cost in Legal Document Review Workflows

1 code implementation18 Jun 2021 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.

Active Learning TAR

Heuristic Stopping Rules For Technology-Assisted Review

no code implementations18 Jun 2021 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.

Active Learning TAR

Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review

no code implementations3 May 2021 Eugene Yang, Sean MacAvaney, David D. Lewis, Ophir Frieder

We indeed find that the pre-trained BERT model reduces review cost by 10% to 15% in TAR workflows simulated on the RCV1-v2 newswire collection.

Active Learning Language Modelling +4

A Sequential Algorithm for Training Text Classifiers

1 code implementation24 Jul 1994 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 is partly or fully textual.

Information Retrieval Retrieval +1

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