Automatic classification of dialogues between clients and a service center needs a preliminary dialogue parameterization. Such a parameterization is usually faced with essential difficulties when we deal with politeness, competence, satisfaction, and other similar characteristics of clients. In the paper, we show how to avoid these difficulties using empirical formulae based on lexical-grammatical properties of a text. Such formulae are trained on given set of examples, which are evaluated manually by an expert(s) and the best formula is selected by the Ivakhnenko Method of Model Self-Organization. We test the suggested methodology on the real set of dialogues from Barcelona railway directory inquiries for estimation of passenger's politeness. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Alexandrov, M., Blanco, X., Ponomareva, N., & Rosso, P. (2007). Constructing empirical models for automatic dialog parameterization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4629 LNAI, pp. 455–463). Springer Verlag. https://doi.org/10.1007/978-3-540-74628-7_59
Mendeley helps you to discover research relevant for your work.