Reference : Populating Legal Ontologies using Information Extraction based on Semantic Role Label...

	Document type :	Dissertations and theses : Doctoral thesis
	Discipline(s) :	Engineering, computing & technology : Computer science Law, criminology & political science : European & international law
	Focus Areas :	Computational Sciences; Law / European Law
	To cite this reference:	http://hdl.handle.net/10993/33810

Title :	Populating Legal Ontologies using Information Extraction based on Semantic Role Labeling and Text Similarity
Language :	English
Author, co-author :	Humphreys, Llio [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	25-Jul-2016
Institution :	University of Luxembourg, ​Luxembourg, ​​Luxembourg
Name of the degree :	Docteur en Informatique
Number of pages :	229
Supervisor :	van der Torre, Leon
	Boella, Guido
	Schommer, Christoph
	Sartor, Giovanni
	Palmirani, Monica
	Baldoni, Matteo
Keywords :	[en] legal informatics ; Law ; Computer Science ; ontologies ; information extraction ; legislation ; recitals ; Cosine Similarity ; Semantic Role Labeling ; structured norms ; compliance ; document management system ; normalisation ; norm types
Abstract :	[en] This thesis seeks to address the problem of the 'resource consumption bottleneck' of creating (legal) semantic technologies manually. It builds on research in legal theory, ontologies and natural language processing in order to semi-automatically normalise legislative text, extract definitions and structured norms, and link normative provisions to recitals. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system. Key contributions are: - an analysis of legislation and structured norms in legal ontologies and compliance systems in order to determine the kind of information that individuals and organisations require from legislation to understand their rights and duties; - an analysis of the semantic and structural challenges of legislative text for machine understanding; - a rule-based normalisation module to transform legislative text into regular sentences to facilitate natural language processing; - a Semantic Role Labeling based information extraction module to extract definitions and norms from legislation and represent them as structured norms in legal ontologies; - an analysis of the impact of recitals on the interpretation of legislative norms; - a Cosine Similarity based text similarity module to link recitals to relevant normative provisions; - a description of important challenges that have emerged from this research which may prove useful for future work in the extraction and linking of information from legislative text.
Research centres :	University of Luxembourg
Funders :	the National Research Fund (Fonds National de la Recherche)
Name of the research project :	3021215
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/33810

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