BERT-GNN APPROACH FOR IDENTIFICATION OF SEMANTIC LEGAL METADATA

Abstract

Legal documents are a crucial part of the regulatory level, governance, and legal decision-making. Seeking and retrieving the semantic legal metadata are crucial in enhancing the understanding of legal documents, automated compliance verification, and legal information smartly retrieved. The previous research regarding legal texts had a number of challenges that included the complex syntax of sentences, a number of words that are specific to the domain, ambiguity, and the lack of explicit semantic relations. In this research, deep learning models such as Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a Natural Language Processing (NLP) model, have been applied for the automated extraction of semantic legal metadata from legal documents. Through the literature review, the research work explores the techniques, data, and models used to analyze legal documents. Transformer-based language models and deep neural network are especially useful in acquiring contextual representations over legal corpora whereas graph-based representations enhance relational insight. To improve precision and scalability of semantic legal metadata extraction, this study will automatically detect legal entities, actions, conditions, and sanctions, removing the need to use human-mediated annotation. The proposed system includes legal research, compliance of regulations, and automated legal knowledge administration. This research improves the efficiency, accuracy, and scalability of the extraction of legal information and assists in intelligent legal analysis and automation of compliance.