no code implementations • 9 Feb 2021 • Rishi Chhatwal, Robert Keeling, Peter Gronvall, Nathaniel Huber-Fliflet, Jianping Zhang, Haozhen Zhao
As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information.
no code implementations • 19 Dec 2019 • Robert Keeling, Rishi Chhatwal, Nathaniel Huber-Fliflet, Jianping Zhang, Fusheng Wei, Haozhen Zhao, Shi Ye, Han Qin
For each data set, classification models were trained with different training sample sizes using different learning algorithms.
no code implementations • 3 Apr 2019 • Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang, Haozhen Zhao
One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those documents.
no code implementations • 3 Apr 2019 • Rishi Chhatwal, Peter Gronvall, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang, Haozhen Zhao
In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision.
no code implementations • 3 Apr 2019 • Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang, Haozhen Zhao
Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review.