Prejudge: A Predictive Analytics System for Crime and Legal Judgments
Keywords:
Citation Analysis; Data Classification; Information Retrieval; Legal Domain; Natural Language Processing; Prediction; Similarity Search.Abstract
The recent era has seen a substantial inflow of legal documents in the electronic format. Given the fact that data mining can be employed in the world of textual data to extract relevant knowledge, it is being prominently exploited in the domain of criminology and legal matters. With increasing crime rates day-by-day, it has become essential to readily impart justice to the victims. It takes a considerable amount of time for the lawyers to go through previous judgments for their research. The judicial process can be accelerated by decreasing the time spent on research work. Smart legal systems have enormous potential for providing significant insights to the legal community and the general public through the use of legal data. As a result, these systems can assist in the analysis and mitigation of a variety of societal concerns. By extracting numerous things from legal decisions, such as dates, case numbers, reference cases, person names, and so on, this work takes the first step toward realizing a smart legal system. The major research issues in the area of applying machine learning in jurisprudence are information extraction and analysis of legal texts. This study proposes an Machine Learning based framework to improve the user's query for retrieval of precisely relevant legal judgments in order to overcome these limitations. This work has been carried out in order to act as an aid to the legal advisors and the lawyers in framing arguments to make strong standpoints based on predictions given on their case pertaining to previous judicial outcomes for similar such cases. Logistic regression-based classification enables efficient retrieval and prediction by allowing inferences based on domain knowledge collected during the dataset development. According to empirical results obtained, the proposed methodology generates finer results than other traditional approaches.
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