Automatic Classification and Analysis of Provisions in Italian Legal Texts: A Case Study

摘要

In this paper we address the problem of automatically enriching legal texts with semantic annotation, an essential pre-requisite to effective indexing and retrieval of legal documents. This is done through illustration of SALEM (Semantic Annotation for LEga Management), a computational system developed for automated semantic annotation of (Italian) law texts. SALEM is an incremental system using Natural Language Processing techniques to perform two tasks: ⅰ) classify law paragraphs according to their regulatory content, and ⅱ) extract relevant text fragments corresponding to specific semantic roles that are relevant for the different types of regulatory content. The paper sketches the overall architecture of SALEM and reports results of a preliminary case study on a sample of Italian law texts.