Automatic Discovery of Semantic Relations using MindNet

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dc.contributor.authorSyed, Zareen
dc.contributor.authorViegas, Evelyne
dc.contributor.authorParastatidis, Savas
dc.date.accessioned2018-11-16T20:13:10Z
dc.date.available2018-11-16T20:13:10Z
dc.date.issued2010-05-19
dc.descriptionProceedings of the Seventh International Conference on Language Resources and Evaluationen_US
dc.description.abstractInformation extraction deals with extracting entities (such as people,organizations or locations) and named relations between entities (such as "People born-in Country") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNeten_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2BK16T26
dc.identifier.urihttp://hdl.handle.net/11603/12038
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student 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.subjectlearningen_US
dc.subjectnatural language processingen_US
dc.subjectSemantic Relationsen_US
dc.subjectMindNeten_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleAutomatic Discovery of Semantic Relations using MindNeten_US
dc.typeTexten_US

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