Case systems abound in natural language processing. Almost any attempt to recognize and uniformly represent relationships within a clause – a unit at the centre of any linguistic system that goes beyond word level statistics – must be based on semantic roles drawn from a small, closed set. The set of roles describing relationships between a verb and its arguments within a clause is a case system. What is required of such a case system? How does a natural language practitioner build a system that is complete and detailed yet practical and natural? This paper chronicles the construction of a case system from its origin in English marker words to its successful application in the analysis of English text.

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We describe a hybrid expert diagnosis-advisory system developed for small and medium enterprises. The Performance, Development, Growth (PDG) system is completely implemented and fully operational, and has been successfully used on real-world data from SMEs for several years. Although our system contains a great deal of the domain knowledge and expertise that is a hallmark of AI systems, it was not designed using the symbolic techniques traditionally used to implement such systems. We explain why this is so and discuss how the PDG system relates to expert systems, decision support systems, and general applications in AI. We also present an experimental evaluation of the system and identify developments currently under way and our plans for the future.

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