Modificatory provisions detection

Abstract

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

Extracting and Connecting Plaintiff S Legal Claims and Judicial Provisions From Brazilian Court Decisions

“…Our proposals differ from previous work (Nguyen et al, 2018, de Araujo et al, 2017, Dragoni et al, 2016, Angelidis et al, 2018, Trompper and Winkels, 2016, Garcia et al, 2017, Boella et al, 2014, Nanda et al, 2017b, Gianfelice et al, 2013, since our corpora are composed of Brazilian lower and Appellate Court decisions, in which, for the first system, we look for a set of modifications commonly provided by the Court, and for the second system, we look for a set of plaintiff's legal claims and judicial provisions commonly judged by the Court. To automatically extract that information, we use a traditional Machine Learning approach and a Deep Learning approach, both as alternative solutions and also as a combined solution.…”

“…The works are ordered by the date of publication. Gianfelice et al propose a system to automatically annotate modificatory provisions in Italian normative texts (Gianfelice et al, 2013). A modificatory provision is a change made to one or more clauses within a text, to the whole text, or to the relations among the constituent provisions of a legal system.…”

Violence detection explanation via semantic roles embeddings

“…Since such embeddings lie in the same semantic space, this approach is used to automatically measure the cross-lingual similarity of language-specific frames to the ends of investigating the possibility of frame transfers across languages. Framebased approaches have been also adopted, paired to deep syntactic analysis, to elaborate documents from the legal domain through a template-filling approach [21,22].…”

Semantic Web for the Legal Domain: The next step

“…43 In 2013, the G8 started the Open Data Charter, 44 also includes legal information as a pillar for enhancing law. For this reason the Open Government Data movement is no longer limited to government organizations but extends its reach to other public bodies, especially to parliamentary bodies (e.g., the Italian Parliament 45 and the US House of Representatives, 46 the Open Government Partnership, 47 and the National Library of Congress of Chile 48 ), and the judiciary (the Open Justice Project in the UK, 49 US Open Data Justice, 50 the California Initiative, 51 the European Court of Justice 52 ). Moreover, thanks to the PSI Directive, the emphasis is also placed on public documents, and not only on public data, enriched with RDF metadata.…”

“…LKIF was used in several projects to model deeper legal concepts [86] and represent legal documents. Several ontologies were developed on top of the work done in CENMetaLex and on Akoma Ntoso: an ontology for managing a legislative text's evolution over time and its linguistic variants [78,79]; an ontology for managing modifications of norms [49,[82][83][84]; an ontology for modelling relations between authorities, agents, and roles in the process of producing a legal document [7]. 55 Architecture for Knowledge-Oriented Management of African Normative Texts using Open Standards and Ontologies, a.k.a., Akoma Ntoso, http://www.akomantoso.org/.…”