Multi-Label Classification on Legal Judgment Prediction Using Legal Bert-Label Powerset
Nasa Zata Dina, Sri Devi Ravana, Norisma Binti Idris · 2024
In recent years, Legal Judgment Prediction (LJP) has attracted a lot of interest from both academic and legal practitioners. Generally, LJP research has three subtasks, i.e., applicable law article prediction, charge/judgment prediction, and term of penalty prediction. Among those three subtasks, the majority of LJP research focuses on charge prediction as one of three tasks of LJP. Charge prediction itself is a single label prediction problem. Unlike the majority, this research proposes a multi-label classification on LJP that predicts violations of relevant law articles in a legal case with the help of Natural Language Processing (NLP). Data used in this research is collected from the publicly accessible legal document in the European Court of Human Rights (ECHR). In detail, the data includes “facts” of each legal case that describes how the events have occurred. Legal BERT (LBERT) embedding is utilized to generate word embedding and Label Powerset is used for problem transformation. Lastly, we apply conventional Machine Learning (ML) classifiers such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) to predict which relevant law articles have been violated. In total, there are 13 law articles in the ECHR dataset. The proposed model achieves over 86.51% precision, 81.77% recall, and 82.89% F1score on ECHR dataset.