Predictive Modeling of Court Decisions using Transformer-Based NLP and Deep Learning Techniques

The high growth in digital legal documents has brought about immense tasks in information retrieval, categorization, and decision-making processes related to law. Manual legal analysis is still time-consuming, inconsistent, and heavily resource-dependent; thus, there is a transition to automated LegalTech. The current study discusses how legislative acts in the European Union (EU) can be predicted based on legal decisions that are made using Natural Language Processing (NLP) and hybrid deep learning. Based on the use of the EurLex corpus with more than 142,000 legislative texts, this study focuses on classifying three major classes: Directives, Regulations, and Decisions. We employ a multi-stage pipeline in order to deal with not only the super-ordinary linguistic diversity of the legal texts but also the imbalance between the masses and categories. We propose and test composite deep learning models that join the Convolutional Neural Networks (CNNs), the Long Short-term memory (LSTM), and the Gated Recurrent Unit (GRU). These have been improved by adding attention mechanisms in order to cover both long-distance and local phrase structures. Its semantical interpretation is also reinforced in the model through FastText embedding that captures the subword law vocabulary. The findings of our experiment suggest hybrid models are more accurate, recall, and F1-score than the traditional baselines of machine learning, especially in terms of multi-class classification of the legislative. It is enhanced by the incorporation of structured metadata, including Act Type and Subject Matter, that adds meaningful value to feature representation and generates a higher level of interpretability. Ignoring the judicial data, instead focusing on legislative data, this study contributes to the use of AI in legal analytics and makes a case for a scalable and explainable document classification approach across linguistic and jurisdictional boundaries.