ENHANCING MULTILABEL CLASSIFICATION IN CHARGE PREDICTION USING LABEL CORRELATION AND PROBLEM TRANSFORMATION METHOD

Abstract

Legal Judgment Prediction (LJP) has recently gained significant interest from both academic and legal practitioners. The majority of LJP methods focus on single label prediction problem, neglecting the real-world multilabel case. Therefore, this study aimed to classify multilabel legal cases using label correlation and problem transformation methods. Data were collected from a publicly accessible legal document in the European Court of Human Rights (ECHR) and EUR-Lex. Multilabel text classification tasks face challenges such as sample diversity, complexity, and the need for effective utilization of label correlations. In this paper, we propose a model that integrates domain specific text embedding and label correlation. Proposed model leverages label powerset as problem transformation to transform a multilabel problem to a multiclass problem by incorporating domain specific text embedding and label correlation, which enhances classification performance in charge prediction and addresses label omission issues. Extensive experiments on two legal text datasets demonstrate the model’s excellent performance. The proposed model substantially outperformed two baseline studies by attaining competitive results of 80.32%-90.09% F1-score and 0.0119-0.0210 Hamming Loss score, respectively. Meanwhile, the baseline models have attained 52%-80% F1-score and 0.0452-0.1479 Hamming Loss score. Proposed model’s performance significantly surpasses the baseline models. The significance of this study is the implementation of label correlation in label powerset problem transformation method and the application of domain specific embedding to solve multilabel classification problem in legal domain.