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%T Concept Embedding for Information Retrieval %A Abdulahhad, Karam %E Pasi, Gabriella %E Piwowarski, Benjamin %E Azzopardi, Leif %E Hanbury, Allan %P 563-569 %V 10772 %D 2018 %I Springer International Publishing %K Computation and Language; Machine Learning %@ 1611-3349 %@ 978-3-319-76941-7 %~ GESIS %> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-70719-0 %X Concepts are used to solve the term-mismatch problem. However, we need an effective similarity measure between concepts. Word embedding presents a promising solution. We present in this study three approaches to build concepts vectors based on words vectors. We use a vector-based measure to estimate inter-concepts similarity. Our experiments show promising results. Furthermore, words and concepts become comparable. This could be used to improve conceptual indexing process. %C DEU %C Cham %G en %9 Konferenzbeitrag %W GESIS - http://www.gesis.org %~ SSOAR - http://www.ssoar.info