Computational Linguistics - Special issue on tense and aspect
Artificial Intelligence - Special volume on natural language processing
The repair of speech act misunderstandings by abductive inference
Computational Linguistics
ITS Tools for Natural Language Dialogue: A Domain-Independent Parser and Planner
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
A framework for robust semantic interpretation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Deriving database queries from logical forms by abductive definition expansion
ANLC '92 Proceedings of the third conference on Applied natural language processing
A fast and portable realizer for text generation systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Discourse relations and defeasible knowledge
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
An efficient incremental architecture for robust interpretation
HLT '02 Proceedings of the second international conference on Human Language Technology Research
A comparison of tutor and student behavior in speech versus text based tutoring
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Content Matters: An Investigation of Feedback Categories within an ITS
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Understanding complex natural language explanations in tutorial applications
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
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The Why-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers via natural language. Although there are inexpensive techniques for analyzing explanations, we claim that better understanding is necessary for use within tutoring systems. In this paper we describe how Why-Atlas creates and utilizes a proof-based representation of student essays. We describe how it creates the proof given the output of sentence-level understanding, how it uses the proofs to give students feedback, some preliminary runtime measures, and the work we are currently doing to derive additional benefits from a proof-based approach for tutoring applications.