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.
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. ... can use large amounts of unlabeled data, a pair of entities may occur multiple times in the test set. …
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