Improving the Quality of Minority Class Identification in Dialog Act Tagging
Year: 2013
Type: article
Abstract: 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%. 1
Institution: Columbia University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2132406104