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. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data

Describing the Ball: Improve Teaching by Using Rubrics - Explicit Grading Criteria

Assessment is crucial to effective teaching and learning. Carnegie\u27s Educating Lawyers and Roy Stuckey\u27s Best Practices for Legal Education emphasize the importance of assessment. This article explains how detailed, written grading criteria describing what students should learn and how they will be evaluated should be a central part of law teachers\u27 assessment plans. The article details how rubrics can improve law student learning, and contains both detailed, step-by-step directions on creating rubrics and examples of rubrics from many different law school courses