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The Automatic Generation of Nonwords for Lexical Recognition Tests

  • Osama HamedEmail author
  • Torsten Zesch
Conference paper
  • 306 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10930)

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.

Keywords

Lexical recognition tests Nonwords generation Words selection Language models 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Language Technology Lab, Department of Computer Science and Applied Cognitive ScienceUniversity of Duisburg-EssenDuisburgGermany

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