Improving the Quality of Minority Class Identification in Dialog Act Tagging

Adinoyi Omuya, Vinodkumar Prabhakaran and Owen Rambow

We present a method of improving the performance of dialog act tagging in identifying minority classes by using per-class feature optimization and a method of choosing the class based not on confidence, but on a cascade of classifiers. We show that it gives a minority class F-measure error reduction of 22.8%, while also reducing the error for other classes and the overall error by about 10%.