Clustering Algorithms
Linguistic Models Construction and Analysis for Satisfaction Estimation
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Constructing empirical models for automatic dialog parameterization
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
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Cluster analysis of dialogs with transport directory service allows revealing the typical scenarios of dialogs, which is useful for designing automatic dialog systems. We show how to parameterize dialogs and how to control the process of clustering. The parameters include both data of transport service and features of passenger s behavior. Control of clustering consists in manipulating the parameter s weights and checking stability of the results. This technique resembles Makagonov s approach to the analysis of dweller s complaints to city administration. We shortly describe B. Stein s new MajorClust method and demonstrate its work on real person-to-person dialogs provided by Spanish railway service.