Syed, ZareenViegas, Evelyne2018-11-132018-11-132010-06-06Zareen 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-Typinghttp://hdl.handle.net/11603/11970Proceedings of the First International Workshop on Formalisms and Methodology for Learning by ReadingThis 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.9 pagesen-USThis 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.information extractionlearningnatural language processingnatural language processingpowersetUMBC Ebiquity Research GroupText