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ACL
2011

In-domain Relation Discovery with Meta-constraints via Posterior Regularization

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In-domain Relation Discovery with Meta-constraints via Posterior Regularization
We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance. 1
Harr Chen, Edward Benson, Tahira Naseem, Regina Ba
Added 23 Aug 2011
Updated 23 Aug 2011
Type Journal
Year 2011
Where ACL
Authors Harr Chen, Edward Benson, Tahira Naseem, Regina Barzilay
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