Legal Party Extraction from Legal Opinion Texts Using Recurrent Deep Neural Networks

Since the advent of deep learning based Natural Language Processing (NLP), diverse domains of human society have benefited form automation and the resultant increment in efficiency. Law and order are, undoubtedly, crucial for the proper functioning of society; for without law there would be chaos, failing to offer equality to everyone. The legal domain being such a vital field, the incorporation of NLP into its workings has drawn attention in many research studies. This study attempts to leverage NLP into the task of extracting legal parties from legal opinion text documents. This task is of high importance given the significance of existing legal cases on contemporary cases under the legal practice, \textit{case law}. This study proposes a novel deep learning methodology which can be effectively used to resolve the problem of identifying legal party members in legal documents. We present two models here, where the first is a BRNN model to detect whether an entity is a legal party or not, and a second, a modification of the same neural network, to classify the thus identified entities into petitioner and defendant classes. Furthermore, in this study, we introduce a novel data set which is annotated with legal party information by an expert in the legal domain. With the use of the said dataset, we have trained and evaluated our models where the experiments carried out support satisfactory performance of our solution. The deep learning model we hereby propose, provides a benchmark for the legal party identification task on this data set. Evaluations for the solution presented in the paper show that our system has 90.89\% precision and 91.69\% recall for legal party extraction from an unseen paragraph from a legal document. As for the classification of petitioners and defendants, we show that GRU-512 obtains the highest F1 score.