Interactive Knowledge Base Population

dc.contributor.authorWolfe, Travis
dc.contributor.authorDredze, Mark
dc.contributor.authorMayfield, James
dc.contributor.authorMcNamee, Paul
dc.date.accessioned2018-11-01T16:16:37Z
dc.date.available2018-11-01T16:16:37Z
dc.date.issued2015-05-15
dc.description.abstractMost work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible. We argue for and describe a new paradigm where the focus is on a high-recall extraction over a small collection of documents under the supervision of a human expert, that we call Interactive Knowledge Base Population (IKBP).en
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/702/Interactive-Knowledge-Base-Populationen
dc.format.extent6 pagesen
dc.genreconference papers and proceedingsen
dc.identifierdoi:10.13016/M2JH3D62J
dc.identifier.urihttp://hdl.handle.net/11603/11824
dc.language.isoenen
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.subjectnatural language processingen
dc.subjectknowledge baseen
dc.subjectinformation extractionen
dc.subjectUMBC Ebiquity Research Groupen
dc.titleInteractive Knowledge Base Populationen
dc.typeTexten

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