This paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in legal document management systems such as Eunomos (Boella et al., 2016).

The DAPRECO knowledge base (D-KB) is a repository of rules written in LegalRuleML, an XML formalism designed to represent the logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR). The D-KB builds upon the Privacy Ontology (PrOnto) (Palmirani et al., 2018), 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 I/O logic (Robaldo and Sun, 2017) and then codified in (LegalRuleML, 2019). To date, the D-KB is the biggest knowledge base in LegalRuleML freely available online at (Robaldo et al., 2019).

In this paper we first introduce the working context related to the understanding of an heterogeneous network of references contained in the Italian regulatory framework. We then present an extended analysis of a large network of laws, providing several types of analytical evaluation that can be used within a legal management system for understanding the data through summarization, visualization, and browsing. In the legal domain, yet several tasks are strictly supervised by humans, with strong consumption of time and energy that would dramatically drop with the help of automatic or semi-automatic supporting tools. We overview different techniques and methodologies explaining how they can be helpful in actual scenarios.

Among the readings available for NL sentences, those where two or more sets of entities are independent of one another are particularly challenging from both a theoretical and an empirical point of view. Those readings are termed here as ‘Independent Set (IS) readings'. Standard examples of such readings are the well-known Collective and Cumulative Readings. (Robaldo, 2011) proposes a logical framework that can properly represent the meaning of IS readings in terms of a set-Skolemization of the witness sets. One of the main assumptions of Robaldo's logical framework, drawn from (Schwarzschild, 1996), is that pragmatics plays a crucial role in the identification of such witness sets. Those are firstly identified on pragmatic grounds, then logical clauses are asserted on them in order to trigger the appropriate inferences. In this paper, we present the results of an experimental analysis that appears to confirm Robaldo's hypotheses concerning the pragmatic identification of the witness sets.

In this paper, we describe how NLP can semi-automate the construction and analysis of knowledge in Eunomos, a legal knowledge management service which enables users to view legislation from various sources and find the right definitions and explanations of legal concepts in a given context. NLP can semi-automate some routine tasks currently performed by knowledge engineers, such as classifying norm, or linking key terms within legislation to ontological concepts. This helps overcome the resource bottleneck problem of creating specialist knowledge management systems. While accuracy is of the utmost importance in the legal domain, and the information should be verified by domain experts as a matter of course, a semi-automated approach can result in considerable efficiency gains.

In this paper, we discuss our analysis and resulting new annotations of Penn Discourse Treebank (PDTB) data tagged as Concession. Concession arises whenever one of the two arguments creates an expectation, and the other ones denies it. In Natural Languages, typical discourse connectives conveying Concession are 'but', 'although', 'nevertheless', etc. Extending previous theoretical accounts, our corpus analysis reveals that concessive interpretations are due to different sources of expectation, each giving rise to critical inferences about the relationship of the involved eventualities. We identify four different sources of expectation: Causality, Implication, Correlation, and Implicature. The reliability of these categories is supported by a high inter-annotator agreement score, computed over a sample of one thousand tokens of explicit connectives annotated as Concession in PDTB. Following earlier work of (Hobbs, 1998) and (Davidson, 1967) notion of reification, we extend the logical account of Concession originally proposed in (Robaldo et al., 2008) to provide refined formal descriptions for the first three mentioned sources of expectations in Concessive relations.

We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus.

This paper describes an approach to Natural Language access to databases based on ontologies. Their role is to make the central part of the translation process independent both of the specific language and of the particular database schema. The input sentence is parsed and the parse tree is semantically annotated via references to the ontology describing the application. This first step is, of course, language dependent: the parsing process depends on the syntax of the language and the annotation depends on the meaning of words, expressed as links between words and concepts in the ontology. Then, the annotated tree is used to produce an “ontological query”, i.e. a query expressed in terms of paths on the ontology. This second step is entirely language- and DB-independent. Finally, the ontological query is translated into a standard SQL query, on the basis of a concept-to-DB mapping, specifying how each concept and relation is mapped onto the database.