Predicting Efficient Outcomes in Trademark and Copyright Cases using NLP Methods

Abstract

Natural Language Processing (NLP) in the legal domain has been a vibrant area of research for many years, while the ability to process text has effectively increased with the development of AI and NLP techniques. Due to the increasing number of court proceedings, particularly those related to Intellectual Property Rights (IPR) in Pakistani judiciary, it is difficult and time-consuming for lawyers to navigate and extract valuable insights from legal data. Thus, there is a growing need for an efficient legal assistance system that can provide major improvements in the efficiency of court procedures. A novel semantic search engine is designed in this research to assist lawyers in managing and drafting IPR cases and extract relevant legal data. This search assistance system can predict court judgments and extract relevant data from Trademark and copyright cases as well as Ordinance Data based on the user’s input query. For judgment forecasting of legal scenarios, XGBoost, SVM, Random Forest (RF) were used, with the mean cross-validation score as 75%, 88%, and 91%. 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, the Mean Average Precisions (MAP) of PAK-LEGAL-BERT and legal-bert-base ranges between 67% to 71%. The models are then fine-tuned on domain-specific data and then used to extract relevant data, thus MAP values increase from 85% to 95% effectively.