Detection of Similar Legal Cases on Personal Injury

The Canadian case system is based on the principle of stare decisis and the concept that like cases should be decided alike. Each judge, when deciding a matter before him or her, selects the prior cases on which to rely. Recently researchers have begun exploring the use of legal text data to find similar cases to assist lawyers with legal research as well as to assist self-represented litigants with legal aid tools. Due to differences in writing style, verbosity, variation in feature importance, case complexity, and subjective bias in judgements, the analysis of legal text using computational models offers interesting challenges for computer scientists. In this study, we explore the problem of finding similar personal injury cases in which plaintiffs claimed compensation specifically for neck and/or back injuries. We extracted and pre-processed unlabeled legal text data and developed deep-learning models across three stages to gradually improve model performance. At each stage, the subset of results was evaluated and validated by a team of lawyers based on qualitative criteria, with the feedback used to refine the model at the next stage. The results demonstrate that semantic similarity between two cases does not ensure that they are legally similar, and artificial intelligence and deep learning techniques for analyzing legal text data can help detect legally similar cases.