NLP-based metadata extraction for legal text consolidation
Pierluigi Spinosa, Gerardo Giardiello, Manola Cherubini, Simone Marchi, Giulia Venturi⋄, Simonetta Montemagni⋄ · 2009
The paper describes a system for the automatic consolidation of Italian legislative texts to be used as a support of an editorial consolidating activity and dealing with the following typology of textual amendments: repeal, substitution and integration. The focus of the paper is on the semantic analysis of the textual amendment provisions and the formalized representation of the amendments in terms of meta-data. The proposed approach to consolidation is metadata--oriented and based on Natural Language Processing (NLP) techniques: we use XML--based standards for metadata annotation of legislative acts and a flexible NLP architecture for extracting metadata from parsed texts. An evaluation of achieved results is also provided.