@inproceedings{humphreys-etal-2020-populating,
title = "Populating Legal Ontologies using Semantic Role Labeling",
author = "Humphreys, Llio and
Boella, Guido and
Di Caro, Luigi and
Robaldo, Livio and
van der Torre, Leon and
Ghanavati, Sepideh and
Muthuri, Robert",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.264",
pages = "2157--2166",
abstract = "This paper is concerned with the goal of maintaining legal information and compliance systems: the {`}resource consumption bottleneck{'} of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract 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 legal document management systems such as Eunomos (Boella et al., 2016).",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>This paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract 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 legal document management systems such as Eunomos (Boella et al., 2016).</abstract>
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%0 Conference Proceedings
%T Populating Legal Ontologies using Semantic Role Labeling
%A Humphreys, Llio
%A Boella, Guido
%A Di Caro, Luigi
%A Robaldo, Livio
%A van der Torre, Leon
%A Ghanavati, Sepideh
%A Muthuri, Robert
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F humphreys-etal-2020-populating
%X This paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract 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 legal document management systems such as Eunomos (Boella et al., 2016).
%U https://aclanthology.org/2020.lrec-1.264
%P 2157-2166
Markdown (Informal)
[Populating Legal Ontologies using Semantic Role Labeling](https://aclanthology.org/2020.lrec-1.264) (Humphreys et al., LREC 2020)
ACL
- Llio Humphreys, Guido Boella, Luigi Di Caro, Livio Robaldo, Leon van der Torre, Sepideh Ghanavati, and Robert Muthuri. 2020. Populating Legal Ontologies using Semantic Role Labeling. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2157–2166, Marseille, France. European Language Resources Association.