The DAPRECO knowledge base is the main outcome of the interdisciplinary project bearing the same name. It is a repository of rules written in LegalRuleML, an XML formalism designed to be a standard for representing the semantic and logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation, the new Regulation that is significantly affecting the digital market in the European Union and beyond. The DAPRECO knowledge base builds upon the Privacy Ontology, which provides (...) a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified Input/Output logic and then codified in LegalRuleML. Reified Input/Output logic is an application of standard Input/Output logic for legal reasoning, in which Input/Output logic is combined with the reification-based approach in Hobbs and Gordon. The DAPRECO knowledge base is then a case study for reified Input/Output logic, and it shows that the formalism indeed appears to be a good candidate to effectively formalize, via uniform and simple representations, complex linguistic/deontic phenomena that may be found in legal texts. To date, the DAPRECO knowledge base is the biggest knowledge base in LegalRuleML and Input/Output logic freely available online. (shrink)

The structures of discourse used by legal and ordinary languages share differences that foster technical issues when applying or fine-tuning general-purpose language models for open-domain question answering on legal resources. For example, longer sentences may be preferred in European laws (i.e., Brussels I bis Regulation EU 1215/2012) to reduce potential ambiguities and improve comprehensibility, distracting a language model trained on ordinary English. In this article, we investigate some mechanisms to isolate and capture the discursive patterns of legalese in order to (...) perform zero-shot question answering, i.e., without training on legal documents. Specifically, we use pre-trained open-domain answer retrieval systems and study what happens when changing the type of information to consider for retrieval. Indeed, by selecting only the important parts of discourse (e.g., elementary units of discourse, EDU for short, or abstract representations of meaning, AMR for short), we should be able to help the answer retriever identify the elements of interest. Hence, with this paper, we publish Q4EU, a new evaluation dataset that includes more than 70 questions and 200 answers on 6 different European norms, and study what happens to a baseline system when only EDUs or AMRs are used during information retrieval. Our results show that the versions using EDUs are overall the best, leading to state-of-the-art F1, precision, NDCG and MRR scores. (shrink)

In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement.