Reference : MULTIMODAL LEGAL INFORMATION RETRIEVAL

	Document type :	Dissertations and theses : Doctoral thesis
	Subtype :	Catalog
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Law / European Law
	To cite this reference:	http://hdl.handle.net/10993/36614

Title :	MULTIMODAL LEGAL INFORMATION RETRIEVAL
Language :	English
Author, co-author :	Adebayo, Kolawole John [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	27-Apr-2018
Institution :	University of Luxembourg, Luxembourg
	Kolawole Adebayo, ​Bologna, ​​Italy
Name of the degree :	DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN INFORMATIQUE
Number of pages :	176
Supervisor :	van der Torre, Leon
	Boella, Guido
President of the jury :	Moens, Marie-Francine
Secretary :	Prakken, Henry
Member of the jury :	Schweighofer, Erich
	Palmirani, Monica
	Di Caro, Luigi
Keywords :	[en] Legal Information Retrieval ; Semantic Annotation ; Information Retrieval
Abstract :	[en] The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely manner. The thesis explored approaches for semantic information retrieval (IR) in the legal context with a technique that maps specific parts of a text to the relevant concept. This technique relies on text segments, using the Latent Dirichlet Allocation (LDA), a topic modeling algorithm for performing text segmentation, expanding the concept using some Natural Language Processing techniques, and then associating the text segments to the concepts using a semi-supervised Text Similarity technique. This solves two problems, i.e., that of user specificity in formulating query, and information overload, for querying a large document collection with a set of concepts is more fine-grained since specific information, rather than full documents is retrieved. The second part of the thesis describes our Neural Network Relevance Model for E-Discovery Information Retrieval. Our algorithm is essentially a feature-rich Ensemble system with different component Neural Networks extracting different relevance signal. This model has been trained and evaluated on the TREC Legal track 2010 data. The performance of our models across board proves that it capture the semantics and relatedness between query and document which is important to the Legal Information Retrieval domain
Research centres :	The Faculty of Sciences, Technology and Communication, University of Luxembourg
Funders :	Erasmus Mundus - EACEA
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/36614

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