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The Automatic Generation of Nonwords for Lexical Recognition Tests

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Human Language Technology. Challenges for Computer Science and Linguistics (LTC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10930))

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Abstract

Lexical recognition tests are frequently used to assess vocabulary knowledge. In such tests, learners need to differentiate between words and artificial nonwords that look much like real words. Our ultimate goal is to create high quality lexical recognition tests automatically which enables repetitive automated testing for different languages. This task involves both simple (words selection) and complex (nonwords generation) subtasks. Our main goal here is to automatically generate word-like nonwords. We compare different ranking strategy and find that our best strategy (a specialized higher-order character-based language model) creates word-like nonwords. We evaluate our nonwords in a user study and find that our automatically generated test yields scores that are highly correlated with a well-established lexical recognition test which was manually created.

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Notes

  1. 1.

    It means generous in English.

  2. 2.

    This is a different size compared to nonwords in LexTALE that are 4 to 11 letters long. In order to ensure comparability with LexTALE, we follow those length constraints, but newly generated tests should use the same constraints for words and nonwords.

  3. 3.

    https://moodle.org.

  4. 4.

    http://www.englishprofile.org/index.php/the-cef.

  5. 5.

    http://elexicon.wustl.edu.

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Author information

Authors and Affiliations

  1. Language Technology Lab, Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Forsthausweg 2, 47057, Duisburg, Germany

    Osama Hamed & Torsten Zesch

Authors
  1. Osama Hamed
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  2. Torsten Zesch
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Corresponding author

Correspondence to Osama Hamed .

Editor information

Editors and Affiliations

  1. Adam Mickiewicz University, Poznań, Poland

    Zygmunt Vetulani

  2. LIMSI-CNRS, Orsay Cedex, France

    Joseph Mariani

  3. Adam Mickiewicz University, Poznań, Poland

    Marek Kubis

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Hamed, O., Zesch, T. (2018). The Automatic Generation of Nonwords for Lexical Recognition Tests. In: Vetulani, Z., Mariani, J., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2015. Lecture Notes in Computer Science(), vol 10930. Springer, Cham. https://doi.org/10.1007/978-3-319-93782-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-93782-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93781-6

  • Online ISBN: 978-3-319-93782-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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