Utterance Classification in Auto Tutor

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA460937

Entities

People

  • Andrew Olney
  • Arthur Graesser
  • Eric Matthews
  • Heather Hite-mitchell
  • Johanna Marineau
  • Max Louwerse

Organizations

  • University of Memphis

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Classification
  • Cognition
  • Cognitive Science
  • Computational Linguistics
  • Computational Science
  • Computers
  • Dialogue Systems
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Natural Language Processing
  • Natural Languages
  • Precision
  • Reliability
  • Students

Readers

  • Computational Linguistics
  • STEM Education