Transcription of Distant supervision for relation extraction without ...
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Distant supervision for relation extraction without labeled dataMike Mintz, Steven Bills, Rion Snow, Dan JurafskyStanford University / Stanford, CA models of relation extraction for tasks likeACE are based on supervised learning of relationsfrom small hand-labeled corpora. We investigate analternative paradigm that does not require labeledcorpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corporaof any size. Our experiments use Freebase, a largesemantic database of several thousand relations, toprovidedistant supervision . For each pair of enti-ties that appears in some Freebase relation , we findall sentences containing those entities in a large un-labeled corpus and extract textual features to traina relation classifier.
text (for example, learning that a person is em-ployed by a particular organization, or that a ge-ographic entity is located in a particular region). In supervised approaches, sentences in a cor-pus are first hand-labeled for the presence of en-tities and the relations between them. The NIST Automatic Content Extraction (ACE) RDC 2003
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