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2021 | OriginalPaper | Chapter

Automatic Short Answer Grading Using Corpus-Based Semantic Similarity Measurements

Authors : Bhuvnesh Chaturvedi, Rohini Basak

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

In this paper, we have explored some unsupervised techniques for the task of Automatic Short Answer Grading. Three models are designed for the task in hand, with each model evaluating and grading students’ responses individually. The proposed method has shown quite promising correlation between scores generated by the method and that awarded by human scorers on a standard computer science dataset, with the overall correlation reaching the peak value at 0.805 which outperforms the state-of-the-art results reported on this dataset so far.

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Footnotes
Literature
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Gomaa, W.H., Fahmy, A.A.: Short answer grading using string similarity and corpus-based similarity. Int. J. Adv. Comput. Sci. Appl. 3(11) (2012)
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Adams, O., Roy, S., Krishnapuram, R.: Distributed vector representations for automatic short answer grading. In: Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications, pp. 20–29 (2016)
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Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
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Metadata
Title
Automatic Short Answer Grading Using Corpus-Based Semantic Similarity Measurements
Authors
Bhuvnesh Chaturvedi
Rohini Basak
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6353-9_24