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Unpublished Paper
Structured Relation Discovery using Generative Models
  • Limin Yao
  • Aria Haghighi
  • Sebastian Riedel
  • Andrew McCallum, University of Massachusetts - Amherst
We explore unsupervised approaches to relation extraction between two named entities; for instance, the semantic \emph{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 up to 7.3% improvement in precision over a state-of-the-art weakly supervised baseline.
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Limin Yao, Aria Haghighi, Sebastian Riedel and Andrew McCallum. "Structured Relation Discovery using Generative Models" (2011)
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