Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments

Jerrold Soh, How Khang Lim, Ian Ernst Chai


Abstract
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.
Anthology ID:
W19-2208
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2019
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
67–77
Language:
URL:
https://aclanthology.org/W19-2208
DOI:
10.18653/v1/W19-2208
Bibkey:
Cite (ACL):
Jerrold Soh, How Khang Lim, and Ian Ernst Chai. 2019. Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 67–77, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments (Soh et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2208.pdf