Reference : MULTIMODAL LEGAL INFORMATION RETRIEVAL
Dissertations and theses : Doctoral thesis
Catalog
Engineering, computing & technology : Computer science
Law / European Law
http://hdl.handle.net/10993/36614
MULTIMODAL LEGAL INFORMATION RETRIEVAL
English
Adebayo, Kolawole John mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
27-Apr-2018
University of Luxembourg, Luxembourg
Kolawole Adebayo, ​Bologna, ​​Italy
DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN INFORMATIQUE
176
van der Torre, Leon mailto
Boella, Guido mailto
Moens, Marie-Francine mailto
Prakken, Henry mailto
Schweighofer, Erich mailto
Palmirani, Monica mailto
Di Caro, Luigi mailto
[en] Legal Information Retrieval ; Semantic Annotation ; Information Retrieval
[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
The Faculty of Sciences, Technology and Communication, University of Luxembourg
Erasmus Mundus - EACEA
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/36614

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