NLP Search Engine for the Legal Ordinance and Cases

The ability to process text has been effectively increased with the development of AI and Natural Language Processing (NLP) techniques. There is a growing need for efficient legal assistance due to the rise in court proceedings, particularly those related to Intellectual Property Rights (IPR). This research aims to develop a novel legal search assistance system to predict legal judgments and extract relevant data from Trademark cases as well as Trademark Ordinance based on user's input query. For judgment forecasting of legal scenarios, XGBoost, SVM and Random Forest (RF) were used, with the mean cross validation score as 67%, 59% and 56%. The use of pre trained BERT model in the designed system further enhances the efficiency of data retrieval. In terms of cases and ordinance data extraction, Mean Average Precisions (MAP) of PAK-LEGAL-BERT and legal-bert-base ranges between 67 % to 71 %, while after fine tuning these models, MAP values increases from 85% to 95% effectively. A user-friendly GUI makes the system accessible to all, which helps in paper drafting of legal cases, irrespective of the legal expertise.