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        <title>Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems</title>
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    <name type="personal">
        <namePart type="given">Florin</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems
%A Godea, Andreea
%A Bulgarov, Florin
%A Nielsen, Rodney
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F godea-etal-2016-automatic
%X 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.
%U https://aclanthology.org/C16-1304
%P 3226-3236

Markdown (Informal)

[Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems](https://aclanthology.org/C16-1304) (Godea et al., COLING 2016)

• Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems (Godea et al., COLING 2016)

ACL

• Andreea Godea, Florin Bulgarov, and Rodney Nielsen. 2016. Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3226–3236, Osaka, Japan. The COLING 2016 Organizing Committee.