NATURAL LANGUAGE PROCESSING FOR LEGAL DOCUMENT ANALYSIS: AUTOMATING JUDICIAL INSIGHTS

Abstract

The exponential growth of legal texts, judgments, and case law databases has created a significant demand for intelligent automation in legal analytics. Natural Language Processing (NLP), a subfield of artificial intelligence, offers robust tools to automate the extraction of judicial insights, analyze legal precedents, and classify court opinions. This paper presents a comprehensive overview of NLP techniques applied to legal document analysis, with a focus on Pakistani legal systems. It covers applications such as case summarization, statute retrieval, and precedent matching. Several case studies and frameworks illustrate the integration of machine learning, deep learning, and rule-based systems in processing unstructured legal texts. This work further highlights the limitations of NLP in handling legal jargon, ambiguity, and multi-language corpora, and proposes strategies for future improvements through hybrid models and legal-specific language models.