| Summary: | Statistical classification techniques for natural-language call routing systems have matured to the point where it is possible to distinguish between several hundreds of semantic categories with an accuracy that is sufficient for commercial deployments. For category sets of this size, the problem of maintaining consistency among manually tagged utterances becomes limiting, as lack of consistency in the training data will degrade performance of the classifier. It is thus essential that the set of categories be structured in a way that alleviates this problem, and enables consistency to be preserved as the domain keeps changing. In this paper, we describe our experiences of using a two-level multi-slot semantics as a way of meeting this problem. Furthermore, we explore the ramifications of the approach with respect to classification, evaluation and dialogue design for call routing systems.
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