Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21530
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dc.contributor.authorAghaebrahimian, Ahmad-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2021-02-04T10:10:08Z-
dc.date.available2021-02-04T10:10:08Z-
dc.date.issued2020-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21530-
dc.description.abstractNamed Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations of a named entity to its formal name. It has been shown that parameterless models for NED do not generalize to other domains very well. On the other hand, parametric learning models do not scale well when the number of formal names expands above the order of thousands or more. To tackle this problem, we propose a deep architecture with superior performance on NED and introduce a strategy to scale it to hundreds of thousands of formal names. Our experiments on several datasets for alias detection demonstrate that our system is capable of obtaining superior results with a large margin compared to other state-of-the-art systems.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMachine learningde_CH
dc.subjectNamed entity disambiguationde_CH
dc.subjectAlias detectionde_CH
dc.subjectDeep learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleNamed entity disambiguation at scalede_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-58309-5_8de_CH
dc.identifier.doi10.21256/zhaw-21530-
zhaw.conference.details9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end110de_CH
zhaw.pages.start102de_CH
zhaw.parentwork.editorSchilling, Frank-Peter-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number12294de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.funding.zhawAuSuM - Automatic Supply Chain Monitoringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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