  In this work, we propose a new method to integrate two recent lines of work:
unsupervised induction of shallow semantics (e.g., semantic roles) and
factorization of relations in text and knowledge bases. 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, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
language.
