Modificatory Provisions Detection: a Hybrid NLP Approach

In the last few years University of Turin and CIRSFID University of Bologna collaborated to pair NLP techniques and legal knowledge to detect modificatory provisions in normative texts. Annotating these modifications is a relevant and interesting problem, in that modifications affect the whole normative system; and legal language, though more regular than unrestricted language, is sometimes particularly convoluted, and poses specific linguistic issues. This paper focuses on two major aspects. First, we explore a combination between parsing and regular expressions; to the best of our knowledge, such hybrid strategy has never been proposed before to tackle the problem at hand. Secondly, we significantly extend past works coverage (basically focussed on substitution, integration and repeal modifications) in order to account for further twelve modification kinds. For the sake of conciseness, we fully illustrate and discuss only few modification types that are more relevant and interesting: suspension, prorogation of efficacy, postponement of efficacy and exception/derogation. These sorts of modifications appear particularly challenging, in that modifications in these categories make use of similar linguistic speech acts and verbs, and exhibit strong similarities in the linguistic syntactical patterns, to such an extent that to discern them is difficult for the legal expert, too. We describe the implemented system and report about an extensive experimentation on the new modificatory provisions. Results are discussed in order to improve both system’s accuracy and annotation practice.