We use structured logistic normal distributions to represent contextual effects.
the work in this paper is inspired by unsupervised approaches that seek to discover types of relations and events, instead of assuming them to be pre-specified; this includes research under various headings such as template/frame/event learning (Cheung et al., 2013;Modi et al., 2012;Chambers and Jurafsky, 2011;Li et al., 2010;Bejan, 2008), script learning (Regneri et al., 2010;Chambers and Jurafsky, 2009), relation learning (Yao et al., 2011), open information extraction (Banko et al., 2007;Carlson et al., 2010), verb caseframe learning (Rooth et al., 1999;Gildea, 2002;Grenager and Manning, 2006;Lang and Lapata, 2010;Ó Séaghdha, 2010;titov and Klementiev, 2012), and a version of frame learning called "unsupervised semantic parsing" (titov and Klementiev, 2011;Poon and Domingos, 2009).