Reference : An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/46243
An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts
English
Sleimi, Amin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Sannier, Nicolas mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sabetzadeh, Mehrdad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Ceci, Marcello mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Dann, John []
In press
Empirical Software Engineering
Kluwer Academic Publishers
Yes (verified by ORBilu)
International
1382-3256
1573-7616
Netherlands
[en] Legal Requirements ; Semantic Legal Metadata ; Natural Language Processing (NLP)
[en] Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97,2% and 82,4%, and recall scores of 94,9% and 92,4%.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Fonds National de la Recherche - FnR
SCARLET
http://hdl.handle.net/10993/46243
FnR ; FNR11801776 > Lionel Briand > SCARLET > Semantic Metadata And Compliance Rule Extraction From Legal Texts > 01/01/2018 > 31/12/2020 > 2017

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