A Machine Learning Framework for Legal Document Recommendations
Year: 2024
Type: article
Abstract: The management of legal documents is a paramount challenge for law firms and legal departments, necessitating efficient retrieval methods to enhance productivity and decision-making. Traditional keyword-based search techniques often fall short in addressing the complexities of legal language and context, leading to inefficiencies in accessing relevant information. This paper presents a novel machine learning framework designed specifically for legal document recommendations, aimed at overcoming the limitations of existing retrieval methods. The framework operates through a multi-stage process encompassing data collection, preprocessing, model training, and recommendation generation, leveraging both supervised and unsupervised learning techniques. This research underscores the transformative potential of machine learning in the legal domain, advocating for its adoption to streamline operations, improve client service, and foster innovation within the legal profession. The findings suggest that ongoing collaboration between legal practitioners and researchers will be crucial in refining these technologies, ensuring their relevance in an evolving legal landscape. Ultimately, this paper calls for a proactive embrace of machine learning solutions to enhance the future of legal document management and research.
DOI: 10.54097/0my1t737
Authors: Mat Andrews, Patrick Bromiley, Elizabeth Chow, Thomas Gibson
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4404722343