Identification of Paragraph Regularities in Legal Judgements Through Clustering and Textual Embedding

In an era characterized by fast technological progresses, working in the law field is very difficult if not supported by the right tools. In this paper, we present a novel method, called JPReg, that identifies paragraph regularities in legal case judgments to support legal experts during the preparation of new legal documents (i.e., paragraphs of existing documents that are similar to those of a document under preparation). JPReg adopts a two-step approach that first clusters similar documents, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Text embedding methods are adopted to represent documents and paragraphs into a numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to those of a target document. Our extensive experimental evaluation, performed on a real-world dataset, shows the effectiveness and the computational efficiency of the proposed method even in presence of noise in the data.