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Structured relation discovery using generative models

Yao, L; Haghighi, A; Riedel, S; McCallum, A; (2011) Structured relation discovery using generative models. In: (pp. pp. 1456-1466).

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We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic bornIn relation between a person and location entity. Concretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of observed triples of entity mention pairs and the surface syntactic dependency path between them. The output of each model is a clustering of observed relation tuples and their associated textual expressions to underlying semantic relation types. Our proposed models exploit entity type constraints within a relation as well as features on the dependency path between entity mentions. We examine effectiveness of our approach via multiple evaluations and demonstrate 12% error reduction in precision over a state-of-the-art weakly supervised baseline. © 2011 Association for Computational Linguistics.

Type: Proceedings paper
Title: Structured relation discovery using generative models
ISBN: 1937284115
UCL classification: UCL > Office of the President and Provost
UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1368699
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