This paper introduces a new representation of sentences--Minimal Meaningful Propositions (MMPS), which allows significant improvement of the mapping between a learner's answer and the ideal response.
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This paper introduces a new representation of sentences--Minimal Meaningful Propositions (MMPS), which allows significant improvement of the mapping between a learner's answer and the ideal response.
Physical Description
11 p.
Notes
Abstract: Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner’s interpretation of a subject and the desired conceptual understanding. In this paper, we take important steps in this direction by introducing a new representation of sentences – Minimal Meaningful Propositions (MMPs), which will allow us to significantly improve the mapping between a learner’s answer and the ideal response. Using this technique, we make significant progress towards highly scalable and domain independent educational systems, that will be able to operate without human intervention. Even though this is a new task, we show very good results both for the extraction of MMPs and for classification with respect to their importance.
Publication Title:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Pages:
11
Page Start:
3226
Page End:
3236
Peer Reviewed:
Yes
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Godea, Andreea; Bulgarov, Florin & Nielsen, Rodney D.Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems,
paper,
December 2016;
Stroudsburg, Pennsylvania.
(https://digital.library.unt.edu/ark:/67531/metadc991471/:
accessed January 17, 2025),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Engineering.
