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ITS
2004
Springer

Combining Competing Language Understanding Approaches in an Intelligent Tutoring System

10 years 9 months ago
Combining Competing Language Understanding Approaches in an Intelligent Tutoring System
When implementing a tutoring system that attempts a deep understanding of students’ natural language explanations, there are three basic approaches to choose between; symbolic, in which sentence strings are parsed using a lexicon and grammar; statistical, in which a corpus is used to train a text classifier; and hybrid, in which rich, symbolically produced features supplement statistical training. Because each type of approach requires different amounts of domain knowledge preparation and provides different quality output for the same input, we describe a method for heuristically combining multiple natural language understanding approaches in an attempt to use each to its best advantage. We explore two basic models for combining approaches in the context of a tutoring system; one where heuristics select the first satisficing representation and another in which heuristics select the highest ranked representation.
Pamela W. Jordan, Maxim Makatchev, Kurt VanLehn
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ITS
Authors Pamela W. Jordan, Maxim Makatchev, Kurt VanLehn
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