99 citations · 105 across the 5 of their papers we have counts for
6 papers
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…
Image Analytics for Legal Document Review: A Transfer Learning Approach
Nathaniel Huber-Fliflet, Fusheng Wei, Haozhen Zhao +3
Though technology assisted review in electronic discovery has been focusing on text data, the need of advanced analytics to facilitate reviewing multimedia content is on the rise.…
Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review
Robert Keeling, Rishi Chhatwal, Nathaniel Huber-Fliflet +5
Research has shown that Convolutional Neural Networks (CNN) can be effectively applied to text classification as part of a predictive coding protocol. That said, most research to d…
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 Study of Deep Learning for Text Classification in Legal Document Review
Fusheng Wei, Han Qin, Shi Ye +1
Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost sign…
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…