  We describe the use of energy function optimization in very shallow syntactic
parsing. The approach can use linguistic rules and corpus-based statistics, so
the strengths of both linguistic and statistical approaches to NLP can be
combined in a single framework. The rules are contextual constraints for
resolving syntactic ambiguities expressed as alternative tags, and the
statistical language model consists of corpus-based n-grams of syntactic tags.
The success of the hybrid syntactic disambiguator is evaluated against a
held-out benchmark corpus. Also the contributions of the linguistic and
statistical language models to the hybrid model are estimated.
