Utterance classification in AutoTutor

Authors:
Andrew Olney;Max Louwerse;Eric Matthews;Johanna Marineau;Heather Hite-Mitchell;Arthur Graesser
Affiliations:
University of Memphis, Memphis, TN;University of Memphis, Memphis, TN;University of Memphis, Memphis, TN;University of Memphis, Memphis, TN;University of Memphis, Memphis, TN;University of Memphis, Memphis, TN
Venue:
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Year:
2003

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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.