Learning representations for information retrieval
Alessandro Sordoni (alessandro (point) sordoni <at> gmail (point) com)
Thursday 5 May 2016 at 1:30 PM — Date et heure inhabituelles - Soutenance de thèse
Salle 3195, Pavillon André-Aisenstadt
Information retrieval deals with questions such as: Is this document relevant to this query? How similar are two queries or two documents? How can query and document similarity be used to enhance relevance estimation? In order to answer these questions, it is necessary to create computational representations of documents and queries. Our goal is to provide new ways of estimating such representations and their relevance relationship. We present a series of research leading to the following observation: future improvements in information retrieval effectiveness has to rely on representation learning techniques instead of a manual definition of the representation space.
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