Polish Court Ruling Classification Using Deep Neural Networks

In this work, the problem of classifying Polish court rulings based on their text is presented. We use natural language processing methods and classifiers based on convolutional and recurrent neural networks. We prepared a dataset of 144,784 authentic, anonymized Polish court rulings. We analyze various general language embedding matrices and multiple neural network architectures with different parameters. Results show that such models can classify documents with very high accuracy (>99%). We also include an analysis of wrongly predicted examples. Performance analysis shows that our method is fast and could be used in practice on typical server hardware with 2 Processors (Central Processing Units, CPUs) or with a CPU and a Graphics processing unit (GPU).