Structured relation discovery using generative models.
EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference.
(pp. 1456 - 1466).
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.
|Title:||Structured relation discovery using generative models|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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