Sifting through hundreds of old case documents to obtain information pertinent to the case in hand has been a major part of the legal profession for centuries. However, with the expansion of court systems and the compounding nature of case law, this task has become more and more intractable with time and resource constraints. Thus automation by Natural Language Processing presents itself as a viable solution. In this paper, we discuss a novel approach for predicting the winning party of a current court case by training an analytical model on a corpus of prior court cases which is then run on the prepared text on the current court case. This will allow legal professionals to efficiently and precisely prepare their cases to maximize the chance of victory. The model is built with and experimented using legal domain specific sub-models to provide more visibility to the final model, along with other variations. We show that our model with critical sentence annotation with a transformer encoder using RoBERTa based sentence embedding is able to obtain an accuracy of 75.75%, outperforming other models.

This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6% compared to the source model’s accuracy in the legal domain.