Abstract
AbstractIn 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.
Funder
ministero dell’istruzione, dell’università e della ricerca
Università degli Studi di Bari Aldo Moro
Publisher
Springer Science and Business Media LLC
Subject
Law,Artificial Intelligence
Reference40 articles.
1. Berkhin P (2002) Survey of clustering data mining techniques. A Survey of Clustering Data Mining Techniques Grouping Multidimensional Data: Recent Advances in Clustering, vol 10
2. Bernhardsson E (2015) Annoy at github. https://github.com/spotify/annoy
3. Biagioli C, Francesconi E, Passerini A, Montemagni S, Soria C (2005) Automatic semantics extraction in law documents. In: The tenth international conference on artificial intelligence and law, proceedings of the conference, June 6-11, 2005, Bologna, Italy, ACM, pp 133–140
4. Brüninghaus S, Ashley K (2001) Improving the representation of legal case texts with information extraction methods. In: Proceedings of the international conference on artificial intelligence and law, pp 42–51
5. Ceci M, Corizzo R, Japkowicz N, Mignone P, Pio G (2020) ECHAD: embedding-based change detection from multivariate time series in smart grids. IEEE Access 8:156053–156066
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