  Building text planning resources by hand is time-consuming and difficult.
Certainly, a number of planning architectures and their accompanying plan
libraries have been implemented, but while the architectures themselves may be
reused in a new domain, the library of plans typically cannot. One way to
address this problem is to use machine learning techniques to automate the
derivation of planning resources for new domains. In this paper, we apply this
technique to build micro-planning rules for preventative expressions in
instructional text.
