Show simple item record

Lexical sense alignment using weighted bipartite b-matching

dc.contributor.authorAhmadi, Sina
dc.contributor.authorArcan, Mihael
dc.contributor.authorMcCrae, John
dc.contributor.editorThierry Declerck and John P. McCrae
dc.date.accessioned2019-06-05T13:28:02Z
dc.date.available2019-06-05T13:28:02Z
dc.date.issued2019-05-20
dc.identifier.citationAhmadi, Sina, Arcan, Mihael, & McCrae, John. (2019). Lexical Sense Alignment using Weighted Bipartite b-Matching. Poster presented at the 2nd Conference on Language, Data and Knowledge (LDK 2019) Leipzig, Germany, 20-23 May.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/15214
dc.description.abstractIn this study, we present a similarity-based approach for lexical sense alignment in WordNet and Wiktionary with a focus on the polysemous items. Our approach relies on semantic textual similarity using features such as string distance metrics and word embeddings, and a graph matching algorithm. Transforming the alignment problem into a bipartite graph matching enables us to apply graph matching algorithms, in particular, weighted bipartite b-matching (WBbM).en_IE
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731015.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherNUI Galwayen_IE
dc.relation.ispartof2nd Conference on Language, Data and Knowledge (LDK 2019)en
dc.subjectLexical sense alignmenten_IE
dc.subjectresource alignmenten_IE
dc.subjecte-lexicographyen_IE
dc.subjectgraph matchingen_IE
dc.titleLexical sense alignment using weighted bipartite b-matchingen_IE
dc.typeConference Posteren_IE
dc.date.updated2019-06-04T11:32:35Z
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderHorizon 2020en_IE
dc.internal.rssid16371367
dc.local.contactSina Ahmadi, The Insight Centre For Data Analytics, National University Of Ireland, Galway , The Deri Building . Email: s.ahmadi1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/731015/EU/European Lexicographic Infrastructure/ELEXISen_IE
nui.item.downloads93


Files in this item

Attribution-NonCommercial-NoDerivs 3.0 Ireland
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

The following license files are associated with this item:

Thumbnail
Name:
LDK_2019_paper_51.pdf
Size:
432.9Kb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record