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

arXiv: https://arxiv.org/abs/1904.06470 · HTML: https://mlsanity.com/paper/1904.06470/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Parameter-Efficient Legal Domain Adaptation](https://mlsanity.com/paper/2210.13712/) (2022)
- [Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models](https://mlsanity.com/paper/2112.07165/) (2021)
- [Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data](https://mlsanity.com/paper/2112.07870/) (2021)
- [Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language](https://mlsanity.com/paper/2110.12383/) (2021)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)
- [Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment](https://mlsanity.com/paper/2405.12910/) (2024)
- [Comparing the Performance of NLP Toolkits and Evaluation measures in Legal Tech](https://mlsanity.com/paper/2103.11792/) (2021)
- [Classification of US Supreme Court Cases using BERT-Based Techniques](https://mlsanity.com/paper/2304.08649/) (2023)
- [A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations](https://mlsanity.com/paper/2309.13051/) (2023)
- [Discovering Significant Topics from Legal Decisions with Selective Inference](https://mlsanity.com/paper/2401.01068/) (2024)
- [A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets](https://mlsanity.com/paper/2409.05972/) (2024)
- [Stop Removing Stopwords: How an Inherited Preprocessing Default Distorts Legal Text-as-Data](https://mlsanity.com/paper/2609.19153/) (2026)

## Abstract (arXiv)

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.