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International Conference on Computational Linguistics and Intelligent Text Processing

CICLing 2017: Computational Linguistics and Intelligent Text Processing pp 655–665Cite as

Automatic Question Generation From Passages

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

Prior work in automatic question generation typically creates questions from sentences in a text. In contrast, the work presented here creates questions from a text passage in a holistic approach to natural language understanding and generation. Several NLP techniques including topic modeling are combined in an ensemble approach to identify important concepts, which then are used to create questions. Evaluation of the generated questions revealed that they are of high linguistic quality and are also important, conceptual questions, compared to questions generated by sentence-level question generation systems.

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Notes

  1. 1.

    https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf.

  2. 2.

    https://textblob.readthedocs.io/en/dev/

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Texas at Dallas, Dallas, TX, 75080, USA

    Karen Mazidi

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  1. Karen Mazidi
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Correspondence to Karen Mazidi .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Mazidi, K. (2018). Automatic Question Generation From Passages. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_49

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  • DOI: https://doi.org/10.1007/978-3-319-77116-8_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77115-1

  • Online ISBN: 978-3-319-77116-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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