Extracting a formal representation from
text that can be used to reason and answer
questions has long been a goal of Artificial
Intelligence research. We demonstrate
a method for knowledge engineers to construct
a semantic interpeter that requires
little natural language processing expertise.
The resulting semantic interpreter
is also able to extend its coverage using
semi-supervised learning. We compare the
performance of an existing semantic interpretation
system to our resulting semantic
interpreter. Our semantic interpreter
shows considerably superior performance
on the two of the three test documents.