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An Empirical Study on Transfer Learning for Privilege Review
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.
Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images
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.
Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review
For each data set, classification models were trained with different training sample sizes using different learning algorithms.
Image Analytics for Legal Document Review: A Transfer Learning Approach
In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry.
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
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.
Empirical Study of Deep Learning for Text Classification in Legal Document Review
Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information.
Empirical Evaluations of Seed Set Selection Strategies for Predictive Coding
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.
