ABSTRACT Since the last few years, computers have become a prominent part of the court of law. Courts generate an enormous amount of unstructured text on a daily basis. Extraction of the desired information from this unstructured legal text is one of the major issues. So, there is a need to develop an intelligent system that can automatically find useful and critical information from the available text. Such a system will help judges and lawyers in their judgments and case preparations, common people in understanding law, and finding appropriate lawyer for their legal issues. Therefore, in this research, Punjab University Legal Mining System (PULMS) is developed using three different supervised machine learning algorithms; conditional random field (CRF), maximum entropy (MaxEnt), and trigram N tag (TNT). To train the system, 304 criminal miscellaneous judgments of the Lahore High Court (LHC) of Pakistan are manually tagged for nine named entities (NE). After training, among three machine learning algorithms, the system achieved significant precision, recall, and f-measure using CRF which are 0.97, 0.87, and 0.89, respectively. Legal text mining, information extraction, named entity recognition, legal proceedings, criminal judgments.
It used CRF and achieves significant precision 0.97, recall 0.87 and f-measure 0.89 REF .
Information Mining From Criminal Judgments of Lahore High Court
IEEE Access
IEEE Access
Computer Science