The increasing Complication and volume of legal Policies pose significant challenges for legal professionals, requiring extra time and effort for analysis, classification, and compliance checks. This research presents an AI-powered Legal Document Analysis System that leverages Natural Language Processing (NLP) and Machine Learning (ML) to automate the extraction, summarization, and classification of legal texts. The system is designed in this way to enhance or achieve efficiency by identifying key legal Policies, detecting bugs, and ensuring regulatory policy compliance. Additionally, it incorporates semantic search and predictive analytics to provide similar legal insights. The proposed model aims to decrease manual workload, minimize errors, and improve decision-making in the legal area domain.Experimental results demonstrate that the system significantly improves accuracy and processing speed compared to traditional methods.
NLP, ML, semantic search, predictive analytics, DL, LLM
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