NooJ Digital Text Automation Workshop Artificial Intelligence (AI): Data Processing Automation and Cybersecurity Technologies

The growing need for efficient and accurate legal information retrieval in Morocco, which utilizes both French and Arabic languages, has fueled the development of Question Answering systems (QAS) tailored for the legal domain. This paper focuses on the crucial initial phase of QAS development, specifically addressing the linguistic analysis required for effective Natural Language Question Analysis (NLQA) in the legal context. The objective is to enhance the system's ability to comprehend and respond to complex legal queries posed in natural language in French. The legal domain poses unique challenges for Natural Language Question Answering (NLQA) systems due to its intricate vocabulary, complex sentence structures, and reliance on specific legal interpretations. Linguistic analysis becomes even more critical in this context, offering a powerful tool to unlock the nuances of legal language and improve question understanding within NLQA systems. The study delves into the unique challenges posed by legal language, characterized by its precision, ambiguity, and reliance on domain-specific terminology. By employing advanced linguistic analysis techniques, including syntactic, semantic, and pragmatic approaches, the paper aims to overcome these challenges and provide a robust foundation for NLQA. Special attention is given to the identification and disambiguation of legal terms, as well as the recognition of syntactic structures and semantic relationships within legal questions. To analyze user questions in natural language, we opted for a pattern-based linguistic approach using the NooJ natural language processing platform. NooJ allows us to build, test, and manage formal descriptions in a wide coverage of natural languages, in the form of dictionaries and electronic grammars.