Improving Translation of Case Descriptions into Logical Fact Formulas using LegalCaseNER

The automated translation of natural language text into structured logical representations is a critical task in various applications, including legal reasoning and decision-making. This paper presents a Name Entity Recognition (NER) based approach for translating the legal case descriptions written in natural language into PROLEG fact formulas. The approach comprises (1) extracting legal entities from the case description using a specialized NER model, namely LegalCaseNER and (2) transforming the extracted entities into PROLEG fact formulas using PROLEG rules. The experimental results demonstrate the efficacy of our proposed approach in accurately extracting relevant entities from legal case descriptions and translating them into the appropriate PROLEG fact formulas. Our approach provides a promising solution for handling complex and diverse case descriptions, enabling their representation in a structured format. This work provides a foundation for future research in the application of logical fact formulas in legal reasoning and decision-making.