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Legal Area Classification: A Comparative Study Of Text Classifiers On Singapore Supreme Court Judgments, Jerrold Soh, How Khang Lim, Ian Ernst Chai

Research Collection Yong Pung How School Of Law

This paper conducts a comparative study on the performance of various machine learning(“ML”) approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.