Abstract: In making legal decisions, courts apply relevant law to facts. While the law typically changes slowly over time, facts vary from case to case. Nevertheless, underlying patterns of fact may emerge. This research focuses on underlying fact patterns commonly present in cases where motorists are stopped for a traffic violation and subsequently detained while a police officer conducts a canine sniff of the vehicle for drugs. We present a set of underlying patterns of fact, that is, factors of suspicion, that police and courts apply in determining reasonable suspicion. We demonstrate how these fact patterns can be identified and annotated in legal cases and how these annotations can be employed to train a transformer model to identify the factors in previously unseen legal opinions.

Bio: Morgan Gray is a PhD student in the Intelligent Systems Program. His research focuses on natural language processing and machine learning, with a focus in text analytics in the legal domain. He is specifically interested in criminal law and procedure.