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Improving document retrieval with a clustering based relevance feedback system

 
: Darms, J.; Dörpinghaus, J.

International Association for Development of the Information Society -IADIS-:
11th IADIS International Conference Information Systems 2018, IS 2018 : Lisbon, Portugal, April 14-16, 2018
Lisbon: IADIS, 2018
ISBN: 978-989-8533-74-6
S.237-240
International Conference Information Systems (IS) <11, 2018, Lisbon>
Englisch
Konferenzbeitrag
Fraunhofer SCAI ()

Abstract
Relevance feedback for document retrieval systems is a technique where user feedback is used to improve a query response. In this work we propose a system that uses multiple clusterings and a semi-supervised heuristic to improve a query response. The heuristic creates an optimal cluster w.r.t. the relevance feedback based on multiple clusterings. We justify the explicit separation of the optimization process and the clustering process by time and space constrains. The evaluation of the heuristic on a corpus containing 1.660 documents from MEDLINE showed promising results. We were able to obtain better results as a single clustering after a few iterations.

: http://publica.fraunhofer.de/dokumente/N-520720.html