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EMNLP
2011

Structured Relation Discovery using Generative Models

8 years 1 months ago
Structured Relation Discovery using Generative Models
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.
Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew
Added 20 Dec 2011
Updated 20 Dec 2011
Type Journal
Year 2011
Where EMNLP
Authors Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew McCallum
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