Utterance Classification in AutoTutor

Authors

• Andrew Olney, University of Memphis
• Max Louwerse, University of Memphis
• Eric Matthews, University of Memphis
• Johanna Marineau, University of Memphis
• Heather Hite-Mitchell, University of Memphis
• Arthur Graesser, University of Memphis

Abstract

This paper describes classification of typed student utterances within AutoTutor, an intelligent tutoring system. Utterances are classified to one of 18 categories, including 16 question categories. The classifier presented uses part of speech tagging, cascaded finite state transducers, and simple disambiguation rules. Shallow NLP is well suited to the task: session log file analysis reveals significant classification of eleven question categories, frozen expressions, and assertions.

Publication Title

Proceedings of the Hlt Naacl 2003 Workshop on Building Educational Applications Using Natural Language Processing

Recommended Citation

Olney, A., Louwerse, M., Matthews, E., Marineau, J., Hite-Mitchell, H., & Graesser, A. (2003). Utterance Classification in AutoTutor. Proceedings of the Hlt Naacl 2003 Workshop on Building Educational Applications Using Natural Language Processing Retrieved from https://digitalcommons.memphis.edu/facpub2/2941