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. Our algorithm combines theadvantages of supervised IE (combining 400,000noisy pattern features in a probabilistic classifier)and unsupervised IE (extracting large numbers ofrelations from large corpora of any domain).

(2007) who extract relations from a Wikipedia page by using supervision from the page’s infobox. Unlike their corpus-specific method, which is spe-cific to a (single) Wikipedia page, our algorithm allows us to extract evidence for a relation from many different documents, and from any genre. 3 Freebase

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  Supervision, Extracts

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