A data visualization and analysis method for natural language call routing system design

Abstract

We describe a data visualization tool that allows a natural language call routing system designer to browse the data from high level routing target classes down to individual sentences. For each target class, automatic clustering creates groups that cluster similar requests. Relabeling data is much more efficient because a cluster of many sentences, instead of individual sentences, can be relabeled in one action. The tool also detects and displays potential confusions between sub-clusters across different classes. The confusability may be caused by erroneous labeling, in which case the entire sub-cluster can be relabeled. If the confusability is inherent, the system designer can design a disambiguation dialogue to clarify the caller's intent.