  A SNoW based learning approach to shallow parsing tasks is presented and
studied experimentally. The approach learns to identify syntactic patterns by
combining simple predictors to produce a coherent inference. Two instantiations
of this approach are studied and experimental results for Noun-Phrases (NP) and
Subject-Verb (SV) phrases that compare favorably with the best published
results are presented. In doing that, we compare two ways of modeling the
problem of learning to recognize patterns and suggest that shallow parsing
patterns are better learned using open/close predictors than using
inside/outside predictors.
