A comparative study of classifying legal documents with neural networks

Abstract

In recent years, deep learning has shown promising results when used in the field of natural language processing (NLP). Neural networks (NNs) such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for various NLP tasks including sentiment analysis, information retrieval, and document classification. In this paper, we the present the Supreme Court Classifier (SCC), a system that applies these methods to the problem of document classification of legal court opinions. We compare methods using traditional machine learning with recent NN-based methods. We also present a CNN used with pre-trained word vectors which shows improvements over the state-of-the-art applied to our dataset. We train and evaluate our system using the Washington University School of Law Supreme Court Database (SCDB). Our best system (word2vec + CNN) achieves 72.4% accuracy when classifying the court decisions into 15 broad SCDB categories and 31.9% accuracy when classifying among 279 finer-grained SCDB categories.