HLTCOE Approaches to Knowledge Base Population at TAC 2009

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dc.contributor.authorMcNamee, Paul
dc.contributor.authorDredze, Mark
dc.contributor.authorGerber, Adam
dc.contributor.authorGarera, Nikesh
dc.contributor.authorFinin, Tim
dc.contributor.authorMayfield, James
dc.contributor.authorPiatko, Christine
dc.contributor.authorRao, Delip
dc.contributor.authorYarowsky, David
dc.contributor.authorDreyer, Markus
dc.date.accessioned2018-11-12T17:01:35Z
dc.date.available2018-11-12T17:01:35Z
dc.date.issued2009-11-01
dc.descriptionProceedings of the 2009 Text Analysis Conference; Gaithersburg, Maryland, USA; November 16-17, 2009en_US
dc.description.abstractThe HLTCOE participated in the entity linking and slot filling tasks at TAC 2009. A machine learning-based approach to entity linking, operating over a wide range of feature types, yielded good performance on the entity linking task. Slot-filling based on sentence selection, application of weak patterns and exploitation of redundancy was ineffective in the slot filling task.en_US
dc.description.urihttps://tac.nist.gov/publications/2009/participant.papers/hltcoe.proceedings.pdfen_US
dc.format.extent10 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/M27940Z4X
dc.identifier.citationPaul McNamee, Mark Dredze, Adam Gerber, Nikesh Garera, Tim Finin, James Mayfield, Christine Piatko, Delip Rao, David Yarowsky, and Markus Dreyer, HLTCOE Approaches to Knowledge Base Population at TAC 2009, Proceedings of the 2009 Text Analysis Conference, 2009,https://tac.nist.gov/publications/2009/participant.papers/hltcoe.proceedings.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/11957
dc.language.isoen_USen_US
dc.publisherNational Institute of Standards and Technologyen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectinformation extractionen_US
dc.subjectknowledge baseen_US
dc.subjectnatural language processingen_US
dc.subjectnatural language processingen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleHLTCOE Approaches to Knowledge Base Population at TAC 2009en_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792

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