Empirical Study of Deep Learning for Text Classification in Legal Document Review
This is an incremental study applying existing deep learning methods to a new domain (legal document review) with practical implications for cost and time savings.
This paper compared deep learning (CNN) with SVM for text classification in legal document review on four real datasets, finding that CNN performed better with larger training datasets and is suitable for the legal industry.
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.