Towards Semantic Classification: An Experimental Study on Automated Understanding of the Meaning of Verbal Utterances

Computationally understanding the meaning of verbal discussions in groups can improve group effectiveness by optimizing their interactions and allocating the needed physical and Cyber resources. Still, it is unknown to what degree the existing Machine Learning (ML) methods can automatically detect the type of verbal utterances, as a preliminary step towards automated meaning understanding. This paper presents a comprehensive experimental study of the performance of the main ML methods in classifying verbal utterances depending on their role during solving programming exercises. A model for interpretable classification using decision trees is also offered. The paper summarizes a set of requirements that new semantic classifiers must satisfy, as current ML methods are likely insufficient for the task. These requirements were experimentally validated.