Empirical Study of Deep Learning for Text Classification in Legal Document Review

Abstract

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.

arXiv comment2018 IEEE International Conference on Big Data (Big Data)

References in corpus (3)

• Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
• Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness
• Empirical Evaluations of Active Learning Strategies in Legal Document Review

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• Comparison of Machine Learning Models to Classify Documents on Digital Development