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Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

Krish Didwania, Durga Toshniwal, Amit Agarwal

TL;DR

The paper addresses the practical challenge of annotating large volumes of legal documents across jurisdictions. It deploys three topic modeling methods—LDA, NMF, and BerTopic—to Indian and UK Supreme Court corpora, with careful preprocessing, input segmentation, and coherence-based evaluation, plus a timeline analysis for India. Key contributions include the first cross-country comparison of long-form legal texts with temporal dynamics, empirical performance benchmarking across the three models, and insights into jurisdiction-specific topic distributions. The work offers a path toward automated, scalable annotation of legal records and informs policy-relevant understanding of judicial discourse across India and the UK.

Abstract

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today's world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and Bertopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years.

Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

TL;DR

The paper addresses the practical challenge of annotating large volumes of legal documents across jurisdictions. It deploys three topic modeling methods—LDA, NMF, and BerTopic—to Indian and UK Supreme Court corpora, with careful preprocessing, input segmentation, and coherence-based evaluation, plus a timeline analysis for India. Key contributions include the first cross-country comparison of long-form legal texts with temporal dynamics, empirical performance benchmarking across the three models, and insights into jurisdiction-specific topic distributions. The work offers a path toward automated, scalable annotation of legal records and informs policy-relevant understanding of judicial discourse across India and the UK.

Abstract

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today's world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and Bertopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years.
Paper Structure (14 sections, 3 equations, 3 figures, 2 tables)

This paper contains 14 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodolody
  4. Dataset
  5. Preprocessing
  6. Topic Modelling
  7. Experimentation
  8. Hyperparameter Tuning
  9. Evaluation Metrics
  10. Results
  11. Quantitative Analysis
  12. Comparison in topics of India and UK
  13. Timeline Analysis of Indian cases
  14. Conclusion and Future Works

Figures (3)

  • Figure 1: Document frequency vs Topic ID Bar graph
  • Figure 2: Heatmaps depicting the interrelations among keywords across various topics within the LDA models: (a) Idealized topic correlations assuming no sequential relationship, (b) Topic correlations observed within the UK context, (c) Topic correlations evident in the India dataset, and (d) Comparative analysis of topic correlations between Indian and UK datasets.
  • Figure 3: Line graphs depicting the count of documents over the years for each topic: (a) Line graphs representing the three most prevalent topics, and (b) Line graphs illustrating the remaining four topics.