Search Results for author:
Found 11 papers, 0 papers with code
Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets
While interesting, manually annotating training text snippets is not generally practical during a legal document review.
CNN Application in Detection of Privileged Documents in Legal Document Review
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.
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.
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.
A Framework for Explainable Text Classification in Legal Document Review
This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents.
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 Evaluations of Active Learning Strategies in Legal Document Review
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.
Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness
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.
Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
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.
An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters
Overly-inclusive keyword searching can also be problematic, because even while it drives up costs, it also can cast `too far of a net' and thus produce unreliable results. To overcome these weaknesses of keyword searching, legal teams are using a new method to target privileged information called predictive modeling.
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.
