Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 27 May 2024.
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5 of 29 references.
References · 5
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2022Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluationcited by 23
- 2024Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family lifecited by 3
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Citing papers per year: 2025: 1.
1 paper.