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

Harr Chen,  Edward Benson,  Tahira Naseem,  Regina Barzilay
MIT CSAIL


Abstract

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.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1054.pdf