Bibtex export
@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}}