7 citations · 7 across the 5 of their papers we have counts for
5 papers
Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets
Christian Mahoney, Peter Gronvall, Nathaniel Huber-Fliflet +1
US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull ma…
Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images
Christian J. Mahoney, Katie Jensen, Fusheng Wei +3
In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests. Errors in pro…
A Framework for Explainable Text Classification in Legal Document Review
Christian J. Mahoney, Jianping Zhang, Nathaniel Huber-Fliflet +2
Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Recently, parties on both sides of the 'legal aisle' are accepting the use…
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
Christian J. Mahoney, Nathaniel Huber-Fliflet, Haozhen Zhao +3
Active learning is a popular methodology in text classification - known in the legal domain as "predictive coding" or "Technology Assisted Review" or "TAR" - due to its potential t…
Empirical Evaluations of Seed Set Selection Strategies for Predictive Coding
Christian J. Mahoney, Nathaniel Huber-Fliflet, Katie Jensen +3
Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the trai…