Neuro-Symbolic AI for Legal Document Reasoning

Abstract — Legal documents are usually long, complex, and difficult to understand for common people. Many individuals face problems because legal language is confusing, legal services are expensive, and court procedures take a long time. Automated systems also struggle to accurately interpret and explain legal documents due to their complex structure and domain specific terminology. Existing artificial intelligence approaches have several limitations when applied to legal document analysis. Deep learning models can understand the meaning of text but cannot explain legal logic clearly, while rule-based systems can apply logic but cannot handle different types of legal language effectively. To solve this problem, this project proposes a Neuro-Symbolic AI system for legal document reasoning. The system combines a transformer-based language model to understand legal text with a rule-based reasoning system to apply legal logic. First, the system analyzes legal documents and extracts important information using a neural model. Then, symbolic rules are applied to perform logical reasoning and generate clear, explainable outcomes. Plain Language Summary: This research introduces a system called Neuro-Symbolic AI for Legal Document Reasoning, designed to make legal documents easier to understand. Legal texts are often long, complex, and difficult for common people to interpret without expert help. This system combines two approaches: one that understands the meaning of legal language (using AI models like LegalBERT) and another that applies logical rules to analyze the content. Keywords – Neuro-Symbolic AI, Legal Document Analysis, Legal Reasoning, Transformer Models, Rule-Based Systems, Explainable AI, Natural Language Processing, Artificial Intelligence in Law, Semantic Analysis