  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.
