  We introduce a new approach to unsupervised estimation of feature-rich
semantic role labeling models. Our model consists of two components: (1) an
encoding component: a semantic role labeling model which predicts roles given a
rich set of syntactic and lexical features; (2) a reconstruction component: a
tensor factorization model which relies on roles to predict argument fillers.
When the components are estimated jointly to minimize errors in argument
reconstruction, the induced roles largely correspond to roles defined in
annotated resources. Our method performs on par with most accurate role
induction methods on English and German, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
languages.
