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.

翻译：法律文书是各国法律实践中不可或缺的组成部分，也是获取过往案例与适用法规信息的主要来源。在当今司法案件数量日益增长的世界中，系统地将历史案例归类为不同子群至关重要，这些分类可为未来案件与实践提供参考。本研究的主要目标，是运用潜在狄利克雷分配、非负矩阵分解及Bertopic等主题建模算法，对来自印度和英国的大量长篇法律文档进行案例标注。这一步骤对于区分两国生成的主题标签至关重要，能够凸显不同司法管辖区所出现案件类型的差异。此外，本研究还对印度案例的时间线进行了分析，以辨明多年来主导主题的演变趋势。