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

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 530–540, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics In-domain Relation Discovery with Meta-constraints via Posterior Regularization Harr Chen, Edward Benson, Tahira Naseem, and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {harr, eob, tahira, regina} @csail.mit.edu Abstract 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, we find that a small set of constraints is applicable across the do- mains, and that using domain-specific con- straints can further improve performance. 1 1 Introduction In this paper, we introduce a novel approach for the unsupervised learning of relations and their instan- tiations from a set of in-domain documents. Given a collection of news articles about earthquakes, for example, our method discovers relations such as the earthquake’s location and resulting damage, and ex- tracts phrases representing the relations’ instantia- tions. Clusters of similar in-domain documents are 1The source code for this work is available at: http://groups.csail.mit.edu/rbg/code/relation extraction/ A strong earthquake rocked the Philippine island of Min- doro early Tuesday, [destroying]ind [some homes]arg ... A strong earthquake hit the China-Burma border early Wednesday ... The official Xinhua News Agency said [some houses]arg were [damaged]ind ... A strong earthquake with a preliminary magnitude of 6.6 shook northwestern Greece on Saturday, ... [destroying]ind [hundreds of old houses]arg ... Figure 1: Excerpts from newswire articles about earth- quakes. The indicator and argument words for the dam- age relation are highlighted. increasingly available in forms such as Wikipedia ar- ticle categories, financial reports, and biographies. In contrast to previous work, our approach learns from domain-independent meta-constraints on rela- tion expression, rather than supervision specific to particular relations and their instances. In particular, we leverage the linguistic intuition that documents in a single domain exhibit regularities in how they express their relations. These regularities occur both in the relations’ lexical and syntactic realizations as well as at the level of document structure. For in- stance, consider the damage relation excerpted from earthquake articles in Figure 1. Lexically, we ob- serve similar words in the instances and their con- texts, such as “destroying” and “houses.” Syntacti- cally, in two instances the relation instantiation is the dependency child of the word “destroying.” On the discourse level, these instances appear toward the beginning of their respective documents. In general, valid relations in many domains are characterized by these coherence properties. …

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