Traditionally in the domain of legal research, the retrieval of pertinent citations from intricate case descriptions has demanded manual effort and keyword-based search applications that mandate expertise in understanding legal jargon. Legal case descriptions hold pivotal information for legal professionals and researchers, necessitating more efficient and automated approaches. We propose a methodology that combines natural language processing (NLP) and machine learning techniques to enhance the organization and utilization of legal case descriptions. This approach revolves around the creation of textual embeddings with the help of state-of-art embedding models. Our methodology addresses two primary objectives: unsupervised clustering and supervised citation retrieval, both designed to automate the citation extraction process. Although the proposed methodology can be used for any dataset, we employed the Supreme Court of The United States (SCOTUS) dataset, yielding remarkable results. Our methodology achieved an impressive accuracy rate of 90.9%. By automating labor-intensive processes, we pave the way for a more efficient, time-saving, and accessible landscape in legal research, benefiting legal professionals, academics, and researchers.

In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain with a case study on refugee law in Canada. Searching case law for past similar cases is a key part of legal work for both lawyers and judges, the potential end-users of our prototype. While traditional named-entity recognition labels such as dates provide meaningful information in legal work, we propose to extend existing models and retrieve a total of 19 useful categories of items from refugee cases. After creating a novel data set of cases, we perform information extraction based on state-of-the-art neural named-entity recognition (NER). We test different architectures including two transformer models, using contextual and non-contextual embeddings, and compare general purpose versus domain-specific pre-training. The results demonstrate that models pre-trained on legal data perform best despite their smaller size, suggesting that domain matching had a larger effect than network architecture. We achieve a F1 score above 90% on five of the targeted categories and over 80% on four further categories.

One of the areas where technology law is likely to see development in South Africa is the regulation of data privacy. The Protection of Personal Information Act (PoPIA) protects personal information and regulates the processing of personal data. However, with the rise of big data and the increasing use of technology in various industries, the legal framework surrounding data privacy will likely evolve in the coming years. This may include changes to PoPIA itself, as well as new legislation and case law that addresses emerging issues in data protection. Another area where tech law will likely see development is regulating artificial intelligence (AI) and machine learning.