Development of an AI-Based Model for Automated Data Extraction and Classification in Legal Documents

Abstract

The problem arises in the legal profession when they have to deal with large volumes of documents, making scrutiny a time-consuming and labour-intensive process. As cases of legal issues continue to escalate, ways to deal with the extraction of legal data are increasingly sought after in efforts to make extraction more efficient and accurate. The paper presents an AI-based model used to extract desired information from legal documents such as metadata and specific fields of data within them. Advanced NLP is applied techniques and machine learning algorithms, the model significantly improves case examination and defect removal efficiency. The presence of domain-specific training data ensures that the model reaches quite acceptable high accuracy, precision, and recall values of relevant information extracted from complex legal texts. In short, it brings immense benefits to legal practitioners through the automation process of data extraction, saving time otherwise allotted towards manual effort. Its accuracy towards identifying petition formats, legal provisions extraction, and contextual features - all these elements contribute highly to the characteristic of this, which is aimed at increased accuracy and fewer discrepancies in the database. The model allows for the better capability of decision-making through strategic planning with reliable and comprehensive data. Overall, this is one solution in AI that brings a new and incredible development in processing legal documents to avoid tedious work, shortcuts in legal workflow processes, to enhance operational performance within the legal sector.