Review of Korean Speech Act Classification: Machine Learning Methods
Harksoo Kim, Choong-Nyoung Seon, Jungyun Seo · Journal of Computing Science and Engineering · 2011
To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.