Reference : Automated Extraction of Semantic Legal Metadata Using Natural Language Processing
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36228
Automated Extraction of Semantic Legal Metadata Using Natural Language Processing
English
Sleimi, Amin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
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) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Dann, John mailto []
Aug-2018
the 26th IEEE International Requirements Engineering Conference, Banff, Alberta, 20-24 August 2018
Yes
International
the 26th IEEE International Requirements Engineering Conference
from 20-08-2018 to 24-08-2018
[en] Legal requirements ; semantic legal metadata ; natural language processing
[en] [Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements. [Objectives] 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 further does not exploit the full potential of natural language processing (NLP). Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the semantic legal metadata types proposed in RE. Subsequently, we conduct a qualitative study aimed at investigating how the identified metadata types can be extracted automatically. [Results and Conclusions] We propose (1) a harmonized conceptual model for the semantic metadata types pertinent to legal requirements analysis, and (2) automated extraction rules for these metadata types based on NLP. We evaluate the extraction rules through a case study. Our results indicate that the rules generate metadata annotations with high accuracy.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Researchers ; Professionals
http://hdl.handle.net/10993/36228
FnR ; FNR11554296 > Mehrdad Sabetzadeh > ARMLET > Automated Retrieval of Metadata from Legal Texts > 01/01/2017 > 31/12/2018 > 2016

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