2009 DistantSupervisionForRE

• (Mintz et al., 2009) ⇒ Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. (2009). “Distant Supervision for Relation Extraction without Labeled Data.” In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL 2009).

Subject Headings: Distant-Learning Algorithm, Freebase.

Cited By

• ~58 http://scholar.google.ca/scholar?hl=en&q=%22Distant+supervision+for+relation+extraction+without+labeled+data%22+2009
• ~18 http://dl.acm.org/citation.cfm?id=1690287&preflayout=flat#citedby

Quotes

Abstract

Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.

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