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dc.contributor.authorYap, Willyen_US
dc.contributor.authorBaldwin, Timothyen_US
dc.date.accessioned2014-05-21T19:13:47Z
dc.date.available2014-05-21T19:13:47Z
dc.date.issued2009en_US
dc.identifier.citationYap, W. & Baldwin, T. (2009). Experiments on pattern-based relation learning. Melbourne: NICTA Victorian Laboratory, Department of CSSE, The University of Melbourne.en_US
dc.identifier.urihttp://hdl.handle.net/11343/25953
dc.description© 2009 Willy Yap & Timothy Baldwinen_US
dc.description.abstractRelation extraction is a sub-task of Information Extraction (IE) that is concerned with extracting semantic relations---such as antonymy, synonymy or hypernymy---between word pairs from corpus data. Past work in relation extraction has concentrated on creating a small set of patterns that are good indicators of whether a word pair contains a semantic relation. In recent years, there has been work on using machine learning to automatically learn these patterns from text. We build on this research in running a series of experiments to investigate the impact of corpus type, corpus size and different parameter settings on learning a range of lexical relations.en_US
dc.languageengen_US
dc.titleExperiments on pattern-based relation learningen_US
dc.typeWorking Paperen_US
melbourne.peerreviewNon Peer Revieweden_US
melbourne.affiliationThe University of Melbourneen_US
melbourne.affiliation.departmentFaculty of Engineering, Computer Science and Software Engineeringen_US
melbourne.publication.statusUnpublisheden_US
melbourne.elementsidNA
melbourne.contributor.authorBaldwin, Timothy
melbourne.accessrightsOpen Access


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