Legal topic classification: A comparative study of text classifiers on Singapore Supreme Court judgments

• Main Authors: SOH, Jerrold Tsin Howe, LIM, How Khang, CHAI, Ian Ernst
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Courts; Science and Technology Law
• Online Access: https://ink.library.smu.edu.sg/sol_research/3049; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5007&context=sol_research
• Institution: Singapore Management University

This paper conducts a comparative study on the performance of various machine learning 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.