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Research has shown that Convolutional Neural Networks (CNN) can beeffectively applied to text classification as part of a predictive codingprotocol. That said, most research to date has been conducted on data sets withshort documents that do not reflect the variety of documents in real worlddocument reviews. Using data from four actual reviews with documents of varyinglengths, we compared CNN with other popular machine learning algorithms fortext classification, including Logistic Regression, Support Vector Machine, andRandom Forest. For each data set, classification models were trained withdifferent training sample sizes using different learning algorithms. Thesemodels were then evaluated using a large randomly sampled test set ofdocuments, and the results were compared using precision and recall curves. Ourstudy demonstrates that CNN performed well, but that there was no singlealgorithm that performed the best across the combination of data sets andtraining sample sizes. These results will help advance research into the legalprofession's use of machine learning algorithms that maximize performance.

Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review