NLP-Based Rule Learning from Legal Text for Question Answering

Abstract

Law is a system containing rules and regulations that binds a people. Legal rules and regulations are usually expressed in domain specific terminologies which are presented in textual form. Its expression is not in machine understandable format for legal reasoning to infer new knowledge or determines if a course of action aligns with the law. Similarly, in order to conceive the rule layer of the semantic web vision in line with the W3C recommendation, in this paper, we present a rule learning technique for learning legal rules for legal question answering, where we learn rules from a collection of instance level triples to infer new rules which can be applied to facts to reason with to arrive at an answer. We explore the natural language processing tool to extract instance level triples from legal textual data and applied RUMIS tool on the extracted triples to produce nonmonotonic rules which are then translated and expressed in Semantic Web Rule Language for legal reasoning in answering legal questions. With the application of the mined rules with our handcrafted rules for legal reasoning, the system was able to answer six out of the thirty correct answers. The research output shows promising results with respect to rule learning for legal reasoning for question answering.