Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System

Myroslava O. Dzikovska, Elaine Farrow, Johanna D. Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an approach for combining symbolic interpretation and statistical classification in the natural language processing (NLP) component of a tutorial dialogue system. Symbolic NLP approaches support dynamic generation of context-adaptive natural language feedback, but lack robustness. In contrast, statistical classification approaches are robust to ill-formed input but provide less detail for context-specific feedback generation. We describe a system design that combines symbolic interpretation with statistical classification to support context-adaptive, dynamically generated natural language feedback, and show that the combined system significantly improves interpretation quality while retaining the adaptivity benefits of a symbolic interpreter.
Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings
EditorsH. Chad Lane, Kalina Yacef, Jack Mostow, Philip Pavlik
PublisherSpringer-Verlag GmbH
Pages279-288
Number of pages10
ISBN (Electronic)978-3-642-39112-5
ISBN (Print)978-3-642-39111-8
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
Volume7926
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Tutorial dialogue
  • natural language processing
  • Intelligent Tutoring System (ITS)
  • parsing
  • semantic interpretation

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