Alternative Title
A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments
Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2019
Abstract
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.
Keywords
natural language processing, text classification, computational analysis of law
Discipline
Asian Studies | Courts | International Law
Research Areas
Innovation, Technology and the Law
Publication
Proceedings of the Natural Legal Language Processing Workshop 2019, Minneapolis, MN, June 7
First Page
67
Last Page
77
Identifier
10.18653/v1/W19-2208
Publisher
Association of Computational Linguistics
City or Country
Minneapolis, MN
Citation
SOH, Jerrold; LIM, How Khang; and CHAI, Ian Ernst.
Legal area classification: A comparative study of text classifiers on Singapore Supreme Court judgments. (2019). Proceedings of the Natural Legal Language Processing Workshop 2019, Minneapolis, MN, June 7. 67-77.
Available at: https://ink.library.smu.edu.sg/sol_research/2956
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/W19-2208
Included in
Asian Studies Commons, Courts Commons, International Law Commons