Concept Embedding for Information Retrieval

Abdulahhad, Karam

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@incollection{ Abdulahhad2018,
 title = {Concept Embedding for Information Retrieval},
 author = {Abdulahhad, Karam},
 editor = {Pasi, Gabriella and Piwowarski, Benjamin and Azzopardi, Leif and Hanbury, Allan},
 year = {2018},
 booktitle = {Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018 ; Proceedings},
 pages = {563-569},
 series = {Lecture Notes in Computer Science (LNCS)},
 volume = {10772},
 address = {Cham},
 publisher = {Springer International Publishing},
 issn = {1611-3349},
 isbn = {978-3-319-76941-7},
 doi = {https://doi.org/10.1007/978-3-319-76941-7_45},
 urn = {https://nbn-resolving.org/urn:nbn:de:0168-ssoar-70719-0},
 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.},
 keywords = {information retrieval; information retrieval; Indexierung; indexing}}