Judgment prediction from legal documents using Texas wolf optimization based deep BiLSTM model

Abstract

The complicated nature of legal texts, a lack of labeled data, concerns about fairness, and difficulties with interpretation represent some of the challenges that judicial judgment prediction models encounter. The approach we propose seeks to conquer these challenges by using advanced techniques for deep learning, such as deep Bidirectional Long Short-Term Memory (BiLSTM) networks to recognize complex linguistic patterns and transfer learning to make more efficient use of data. Employing a deep BiLSTM classifier (TWO-BiLSTM) model based on Texas wolf optimization, the research aims to predict legal judgments. To prepare it for evaluation, it initially collects and preprocesses judicial data. Feature extraction involves statistical and Principal component Analysis (PCA) techniques to generate an extensive feature set. The model undergoes training utilizing these features in addition to preprocessed data. A hybrid Texas wolf optimization tactic, based on the optimization of gray wolves and Harris hawks, is employed to boost performance. The ability of the model to accurately and effectively predict legal judgment has been demonstrated by testing it on different sets of judicial data. The model achieved reasonably well in TP 90, having an accuracy of 97.00%. It also achieved exceedingly well in f-score, precision, and recall, having scores of 97.29, 97.10, and 97.19, correspondingly. The model’s effectiveness was further demonstrated in the k-fold 10 assessment, which exhibited 96.00% accuracy and robustness. In addition, using f-score, precision, and recall metrics of 96.25, 96.89, and 95.96, respectively, the model showed outstanding performance. These outstanding results demonstrate the model’s effectiveness and dependability for providing accurate predictions.