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Artificial Intelligence and Law
Erschienen in: Artificial Intelligence and Law 2/2019

13.12.2018

Semi-automatic knowledge population in a legal document management system

verfasst von: Guido Boella, Luigi Di Caro, Valentina Leone

Erschienen in: Artificial Intelligence and Law | Ausgabe 2/2019

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Abstract

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 relevant definitions, and extracting key elements of legal provisions to facilitate clarity and advanced search options. The use of Natural Language Processing tools to semi-automate such tasks makes the proposal a realistic commercial prospect as it helps keep costs down while allowing greater coverage.
Vorheriger Artikel Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives

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Fußnoten
4
The Arianna portal already exports documents to NIR XML format.
 
6
We specified a maximum distance of 2 words in order to encompass both sentences of the form ‘Il rif1 è soppresso’ (The rif1 is suppressed) and sentences of the form ‘Il rif1 è stato soppresso’ (The rif1 has been suppressed). In Italian, the lemma of both words ‘è’ and ‘stato’ is ‘essere’.
 
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Metadaten
Titel
Semi-automatic knowledge population in a legal document management system
verfasst von
Guido Boella
Luigi Di Caro
Valentina Leone
Publikationsdatum
13.12.2018
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence and Law / Ausgabe 2/2019
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI
https://doi.org/10.1007/s10506-018-9239-8

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