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.

In this paper we present a novel method for the automatic classification of multi-label text documents. In principle, automatic classification of text is usually tackled by supervised Machine Learning techniques like Support Vector Machines (SVM), that typically achieve state-of-the-art accuracy in several domains. Nevertheless, SVM can not handle multi-labeled documents, thus a specific preprocessing of the data is needed. In this paper we present a novel technique for the transformation of multi-label data into mono-label that is able to maintain all the information, allowing the use of standard approaches like SVM. We then evaluate our system using JRC-Acquis-it, a large dataset of italian legislation that has been manually annotated according to EuroVoc, demonstrating the potential of our approach compared to the current state of the art.

In this paper we present a prototype for automatically identifying and classifying types of modifications in Italian legal text. The prototype is part of the Eunomos system, a legal knowledge management service that integrates and makes available legislation from various sources, while finding definitions and explanations of legal concepts in a given context. The design of the prototype is grounded on the error analysis of a previous prototype. The latter made use of dependency relations provided by the TUP parser, a multi-purpose parser for Italian. Since those syntactic relations were responsible of the majority of errors, we decided in the present tool to ignore them, and to rewrite an ad-hoc shallow parsing, based on the morphological analysis of the legal text (still provided by the TUP parser). We obtained performances much greater than those of the initial prototype. In particular, the level of precision of the classification in output is now close to 100%.

We are concerned with the automatic semantic interpretation of legal modificatory provisions. We propose a novel approach which pairs deep syntactic parsing and a fine-grained taxonomy of legal modifications. Although still in a developmental stage, the implemented system can be used to annotate with meta-information modificatory provisions of NormaInRete documents.

This paper describes the philosophy behind our tool called "Legal Taxonomy Syllabus", the analytical instruments it provides and some case studies. The Legal Taxonomy Syllabus is an ontology based tool designed to annotate and recover multi-lingua legal information and build conceptual dictionaries. The Legal Taxonomy Syllabus allows to build legal dictionaries in a bottom up fashion starting from the annotation of legal terms by legal terminological experts and to let legal ontology engineers refine the resulting taxonomies of concepts. The Legal Taxonomy Syllabus and its analytical tools provide help to lawyers to study the peculiarities of European Union Directives concerning the polysemy of legal terms, and the terminological and conceptual misalignment. By means of two case studies we show how the Legal Taxonomy Syllabus can help the processes of drafting and translating of the Directives.

This paper aims at linking the AI notion of action to the notion of LEGAL RELATION. Legal rules concern actions to accomplish or to exclude and states to achieve or to avoid. The body of the rule establishes who and under which conditions must respect the obligation. In this paper, we introduce a definition of obligation and use it as an ontological basis to describe the LEGAL RELATIONS appearing in the A-Hohfeld language.

The aim of the present paper is to show how a rule-based parser for the Italian language has been extended to analyze sentences involving conjunctions. The most noticeable fact is the ease with which the required modifications fit in the previous parser structure. In particular, the rules written for analyzing simple sentences (without conjunctions) needed only small changes. On the contrary, more substantial changes were made to the exception-handling rules (called "natural changes") that are used to restructure the tree in case of failure of a syntactic hypothesis. The parser described in the present work constitutes the syntactic component of the FIDO system (a Flexible Interface for Database Operations), an interface allowing an end-user to access a relational database in natural language (Italian).

The paper discusses three different kinds of syntactic ill-formedness: ellipsis, conjunctions, and actual syntactic errors. It is shown how a new grammatical formalism, based on a two-level representation of the syntactic knowledge is used to cope with ill-formed sentences. The basic control structure of the parser is briefly sketched; the paper shows that it can be applied without any substantial change both to correct and to ill-formed sentences. This is achieved by introducing a mechanism for the hypothesization of syntactic structures, which is largely independent of the rules defining the well-formedness. On the contrary, the second level of syntactic knowledge embodies those rules and is used to validate the hypotheses emitted by the first level. Alternative hypotheses are obtained, when needed, by means of local reorganizations of the parse tree. Sentence fragments are handled by the same mechanism, but in this case the second level rules are used to detect the absence of one (or more) constituents.

The paper deals with the representation of lexical knowledge in terms of phonetic and phonemic features. The representation is based on a set of frames containing preconditions and actions for each word. Preconditions are compounded in an automaton allowing a fast detection of hypothesizable words.