Learning Micro-Planning Rules for Preventive Expressions
Keith Vander Linden, Barbara Di Eugenio · 1996
Building text planning resources by hand is timeconsuming 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 microplanning rules for preventative expressions in instructional text.