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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