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

ACL ID P11-1054
Title In-domain Relation Discovery with Meta-constraints via Posterior Regularization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We present a novel approach to discovering re- lations and their instantiations from a collec- tion 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, includ- ing 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 recov- ers hidden relation structure, comparable to or outperforming previous state-of-the-art ap- proaches. Furthermore...