Techniques to estimate the status of legal proceedings considering sequential text data

Abstract

The relationship between law and natural language processing is impressively expanding, and it has the potential to fundamentally alter how prosecutors go about their regular tasks. The volume of text produced by legal practitioners is enormous and has not yet been fully inves-tigated by data science. Recent developments in NLP and machine learning allow us the capability to develop forecasting analytics that can be used to identify trends influencing judicial judgments. This study's objective is to generate a classifier that can anticipate judicial decisions in Bangladeshi Supreme Court law. According to our exploratory research, the fundamental facts of a case are the most significant predictor. Then, some of their traits were ascertained from a small sample of judicial subjects. Then, taking these language qualities into consideration, a variety of classifiers were used to forecast the legal results.