9 found
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  1.  54
    The European Legal Taxonomy Syllabus: A Multi-Lingual, Multi-Level Ontology Framework to Untangle the Web of European Legal Terminology.Gianmaria Ajani, Guido Boella, Luigi di Caro, Livio Robaldo, Llio Humphreys, Sabrina Praduroux, Piercarlo Rossi & Andrea Violato - 2016 - Applied ontology 11 (4):325-375.
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  2.  15
    Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives.Rohan Nanda, Giovanni Siragusa, Luigi Di Caro, Guido Boella, Lorenzo Grossio, Marco Gerbaudo & Francesco Costamagna - 2019 - Artificial Intelligence and Law 27 (2):199-225.
    The automated identification of national implementations of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (...)
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  3.  11
    Semi-Automatic Knowledge Population in a Legal Document Management System.Guido Boella, Luigi Di Caro & Valentina Leone - 2019 - Artificial Intelligence and Law 27 (2):227-251.
    Every organization has to deal with operational risks, arising from the execution of a company’s primary business functions. In this paper, we describe a legal knowledge management system which helps users understand the meaning of legislative text and the relationship between norms. While much of the knowledge requires the input of legal experts, we focus in this article on NLP applications that semi-automate essential time-consuming and lower-skill tasks—classifying legal documents, identifying cross-references and legislative amendments, linking legal terms to the most (...)
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  4.  8
    Taking stock of legal ontologies: a feature-based comparative analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies (...)
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  5.  4
    Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives.Rohan Nanda, Giovanni Siragusa, Luigi Di Caro, Guido Boella, Lorenzo Grossio, Marco Gerbaudo & Francesco Costamagna - 2019 - Artificial Intelligence and Law 27 (2):199-225.
    The automated identification of national implementations of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (...)
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  6.  10
    Taking Stock of Legal Ontologies: A Feature-Based Comparative Analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies (...)
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  7.  5
    Taking stock of legal ontologies: a feature-based comparative analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies (...)
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  8.  1
    Semi-automatic knowledge population in a legal document management system.Guido Boella, Luigi Di Caro & Valentina Leone - 2019 - Artificial Intelligence and Law 27 (2):227-251.
    Every organization has to deal with operational risks, arising from the execution of a company’s primary business functions. In this paper, we describe a legal knowledge management system which helps users understand the meaning of legislative text and the relationship between norms. While much of the knowledge requires the input of legal experts, we focus in this article on NLP applications that semi-automate essential time-consuming and lower-skill tasks—classifying legal documents, identifying cross-references and legislative amendments, linking legal terms to the most (...)
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  9.  6
    Populating legal ontologies using semantic role labeling.Llio Humphreys, Guido Boella, Leendert van der Torre, Livio Robaldo, Luigi Di Caro, Sepideh Ghanavati & Robert Muthuri - 2021 - Artificial Intelligence and Law 29 (2):171-211.
    This article seeks to address the problem of the ‘resource consumption bottleneck’ of creating legal semantic technologies manually. It describes a semantic role labeling based information extraction system to extract definitions and 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 a legal document management system.
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