Abstract
One of the main emerging challenges in legal documentation is to capture the meaning and the semantics of normative content using NLP techniques, and to isolate the relevant part of the linguistic speech. The last five years have seen an explosion in XML schemas and DTDs whose focus in modelling legal resources their focus was on structure. Now that the basic elements of textual descriptiveness are well formalized, we can use this knowledge to proceed with content. This paper presents a detailed methodology for classifying modificatory provisions in depth and providing all the necessary information for semi-automatically managing the consolidation process. The methodology is based on an empirical legal analysis of about 29,000 Italian acts, where we bring out regularities in the language associated with some modifications, and where we define patterns of proprieties for each type of modificatory provision. The list of verbs and the frames inferred through this empirical legal analysis have been used by the NLP group at the University of Turin to refine a syntactical NLP parser for isolating and representing the sentences as syntactic trees, and the pattern will be used by the light semantic interpreter module to indentify the parameters of modificatory provisions.
This paper was carried out with the following authors’ contributions: M. Palmirani paragraphs 1, 2, 6, 7, appendix; R. Brighi paragraphs 3, 4, 5.
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Palmirani, M., Brighi, R. (2010). Model Regularity of Legal Language in Active Modifications. In: Casanovas, P., Pagallo, U., Sartor, G., Ajani, G. (eds) AI Approaches to the Complexity of Legal Systems. Complex Systems, the Semantic Web, Ontologies, Argumentation, and Dialogue. AICOL 2009. Lecture Notes in Computer Science(), vol 6237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16524-5_5
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DOI: https://doi.org/10.1007/978-3-642-16524-5_5
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