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?:about
?:abstract
  • 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. (xsd:string)
?:contributor
?:dateModified
  • 2018 (xsd:gyear)
?:datePublished
  • 2018 (xsd:gyear)
?:doi
  • 10.1007/978-3-319-76941-7_45 ()
?:duplicate
?:editor
?:hasFulltext
  • true (xsd:boolean)
is ?:hasPart of
?:inLanguage
  • en (xsd:string)
?:isbn
  • 978-3-319-76941-7 ()
?:issn
  • 1611-3349 ()
?:linksDOI
?:linksURN
?:location
is ?:mainEntity of
?:name
  • Concept Embedding for Information Retrieval (xsd:string)
?:provider
?:publicationType
  • Konferenzbeitrag (xsd:string)
?:publisher
?:sourceCollection
  • Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018 ; Proceedings (xsd:string)
?:sourceInfo
  • GESIS-SSOAR (xsd:string)
rdf:type
?:url
?:urn
  • urn:nbn:de:0168-ssoar-70719-0 ()
?:volumeNumber
  • 10772 (xsd:string)