An important part of building a natural-language generation (NLG) system is
knowledge acquisition, that is deciding on the specific schemas, plans, grammar
rules, and so forth that should be used in the NLG system. We discuss some
experiments we have performed with KA for content-selection rules, in the
context of building an NLG system which generates health-related material.
These experiments suggest that it is useful to supplement corpus analysis with
KA techniques developed for building expert systems, such as structured group
discussions and think-aloud protocols. They also raise the point that KA issues
may influence architectural design issues, in particular the decision on
whether a planning approach is used for content selection. We suspect that in
some cases, KA may be easier if other constructive expert-system techniques
(such as production rules, or case-based reasoning) are used to determine the
content of a generated text.Comment: To appear in the 1997 European NLG workshop. 10 pages, postscrip