  Semantic roles play an important role in extracting knowledge from text.
Current unsupervised approaches utilize features from grammar structures, to
induce semantic roles. The dependence on these grammars, however, makes it
difficult to adapt to noisy and new languages. In this paper we develop a
data-driven approach to identifying semantic roles, the approach is entirely
unsupervised up to the point where rules need to be learned to identify the
position the semantic role occurs. Specifically we develop a modified-ADIOS
algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and
use these grammar structures to learn the rules for identifying the semantic
roles based on the context in which the grammar structures appeared. The
results obtained are comparable with the current state-of-art models that are
inherently dependent on human annotated data.
