2010 ModelingRelationsandtheirMentio

• (Riedel et al., 2010) ⇒ Sebastian Riedel, Limin Yao, and Andrew McCallum. (2010). “Modeling Relations and their Mentions Without Labeled Text.” In: Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases (ECML/PKDD-2010).

Subject Headings: Relation Recognition Algorithm, Distance-based Semi-Supervised Training.

Cited By

• http://scholar.google.com/scholar?q=%22Modeling+relations+and+their+mentions+without+labeled+text%22+2011
• http://dl.acm.org/citation.cfm?doid=&preflayout=flat#citedby

Quotes

Abstract

Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31% error reduction.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 ModelingRelationsandtheirMentio	Limin Yao Sebastian Riedel Andrew McCallum			Modeling Relations and their Mentions Without Labeled Text						2010