Learning representations for information retrieval

Alessandro Sordoni (alessandro (point) sordoni <at> gmail (point) com)


Le 5 mai 2016 à 13 h 30 — 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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