A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing

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dc.contributor.authorSyed, Zareen
dc.contributor.authorViegas, Evelyne
dc.date.accessioned2018-11-13T16:22:39Z
dc.date.available2018-11-13T16:22:39Z
dc.date.issued2010-06-06
dc.descriptionProceedings of the First International Workshop on Formalisms and Methodology for Learning by Readingen_US
dc.description.abstractThis paper describes a hybrid approach for unsupervised and unrestricted relation discovery between entities using output from linguistic analysis and semantic typing information from a knowledge base. We use Factz (encoded as subject, predicate and object triples) produced by Powerset as a result of linguistic analysis. A particular relation may be expressed in a variety of ways in text and hence have multiple facts associated with it. We present an unsupervised approach for collapsing multiple facts which represent the same kind of semantic relation between entities. Then a label is selected for the relation based on the input facts and entropy based label ranking of context words. Finally, we demonstrate relation discovery between entities at different levels of abstraction by leveraging semantic typing information from a knowledge base.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typingen_US
dc.format.extent9 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2JM23K89
dc.identifier.citationZareen Syed and Evelyne Viegas, A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing, Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 105–113, 2010 , https://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typingen_US
dc.identifier.urihttp://hdl.handle.net/11603/11970
dc.language.isoen_USen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
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.subjectinformation extractionen_US
dc.subjectlearningen_US
dc.subjectnatural language processingen_US
dc.subjectnatural language processingen_US
dc.subjectpowerseten_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleA Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typingen_US
dc.typeTexten_US

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