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Model Answer Generation for Word-Type Questions in Elementary Mathematics

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Natural Language Processing and Information Systems (NLDB 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11608))

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Abstract

There are several categories of word-type questions at elementary level Mathematics. These include addition, subtraction, multiplication, division and ratio. Addition and subtraction problems can be further divided based on their textual information. Those types are change type (join-separate type), compare type, and whole-part type. This paper presents a set of ensemble classifiers to automatically generate model answers for these three types of addition and subtraction problems. Currently, questions with one unknown variable are considered. In addition to the existing data sets, a new data set is created for the training and the evaluation purpose. Our results outperform the existing statistical approaches.

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Acknowledgement

This research was funded by a Senate Research Committee (SRC) Grant of University of Moratuwa.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering, University of Moratuwa, Katubedda, 10400, Sri Lanka

    Sakthithasan Rajpirathap & Surangika Ranathunga

Authors
  1. Sakthithasan Rajpirathap
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  2. Surangika Ranathunga
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Corresponding authors

Correspondence to Sakthithasan Rajpirathap or Surangika Ranathunga .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, UK

    Farid Meziane

  3. University of Salford, Salford, UK

    Sunil Vadera

  4. Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  5. CSE, University of Salford, Salford, UK

    Mohamad Saraee

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Rajpirathap, S., Ranathunga, S. (2019). Model Answer Generation for Word-Type Questions in Elementary Mathematics. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-23281-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23280-1

  • Online ISBN: 978-3-030-23281-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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