PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments

Abstract

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data.

Regression applied to legal judgments to predict compensation for immaterial damage

“…The numbers represent some information about each word in the text, for example, the term frequency (TF) ( Baeza-Yates & Ribeiro-Neto, 1999 ). Beyond BOW model, there are word embeddings ( Pennington, Socher & Manning, 2014 ; Bojanowski et al, 2016 ), topic modeling ( Blei, Ng & Jordan, 2003 ; Kherwa & Bansal, 2017 ), and many others ( Devlin et al, 2018 ; Peters et al, 2018 ; Brown et al, 2020 ; Pittaras et al, 2020 ; Dhanani, Mehta & Rana, 2022 ; Martino, Pio & Ceci, 2021 ; Chalkidis et al, 2020 ).…”