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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
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