no code implementations • 16 Dec 2021 • Haozhen Zhao, Shi Ye, Jingchao Yang
Given the generalizable nature of privilege in legal cases, we hypothesize that transfer learning can capitalize knowledge learned from existing labeled data to identify privilege documents without requiring labeling new training data.
no code implementations • 5 Feb 2021 • Christian J. Mahoney, Katie Jensen, Fusheng Wei, Haozhen Zhao, Han Qin, Shi Ye
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.
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 • 19 Dec 2019 • Nathaniel Huber-Fliflet, Fusheng Wei, Haozhen Zhao, Han Qin, Shi Ye, Amy Tsang
In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry.
no code implementations • 11 Jun 2019 • Christian J. Mahoney, Nathaniel Huber-Fliflet, Haozhen Zhao, Jianping Zhang, Peter Gronvall, Shi Ye
In this study, we use extensive experimentation to examine the impact of popular seed set selection strategies in active learning, within a predictive coding exercise, and evaluate different active learning strategies against well-researched continuous active learning strategies for the purpose of determining efficient training methods for classifying large populations quickly and precisely.
no code implementations • 3 Apr 2019 • Fusheng Wei, Han Qin, Shi Ye, Haozhen Zhao
Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information.
no code implementations • 21 Mar 2019 • Christian J. Mahoney, Nathaniel Huber-Fliflet, Katie Jensen, Haozhen Zhao, Robert Neary, Shi Ye
Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well.