Transcription of Distant supervision for relation extraction without ...
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Distant supervision for relation extraction without ...
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.
pus are first hand-labeled for the presence of en-tities and the relations between them. The NIST Automatic Content Extraction (ACE) RDC 2003 and 2004 corpora, for example, include over 1,000 documents in which pairs of entities have been la-beled with 5 to 7 major relation types and 23 to 24 subrelations, totaling 16,771 relation instances.
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