Introducing Intelligence to the Semantic Analysis of Canadian Maritime Case Law: Case Based Reasoning Approach

Abstract

The use of machine learning and semantic analysis in case law is the new trend in modern society. Case Based Reasoning tools are being used to analyze texts in courts to make and predict judicial decisions which are designed to base the outcomes of current court proceedings from past and or learning from the mistakes to make better decisions. Because of the accuracy and speed of this technology, researchers in the justice system have introduced Machine Learning to optimize the Case-Based Researching approach. This paper presents a study aimed to critically analyze semantic analysis in the context of machine learning and proposes a case-based reasoning information retrieval system. It will explore how CBR-IR is being used to improve legal case law information retrieval. The study covers the importance of semantic analysis. The study will discuss limitations and recommendations for improvement and future research. The study recommends that it is necessary to conduct further research in semantic analysis and how they can be used to improve information retrieval of Canadian maritime case law.