Home > English, German, Italian, service-is-learning-materials-creation, service-is-programming, Spanish > Using home-brew NLP regular expressions to automate question generation for learning material creation

Using home-brew NLP regular expressions to automate question generation for learning material creation

    1. The trpQuizGenerator, from which the screenshots below are taken,
      1. is an attempt to facilitate, speed up, automate question generation for foreign language learning by collecting a regular expressions, reflecting typical patterns that cause difficulties for language learners in a number of L2 – inspired by common 1st and 2nd year textbooks:  trpQuizGenerator-NLP-samples-German-Italian-Spanish
      2. German: differentiation between Dative and Accusative case personal pronouns
      3. Italian: contraction of article and preposition
      4. Spanish: demonstrative pronouns.
      5. Some more rather arbitrary, but easily implemementable examples for ESL:
        1. Numbers: ―Much/many‖ dichotomy
        2. which/who‖ relative pronoun dichotomy: Difficult for German students of English which has no such
          dichotomy for innate beings/things, but whose (antiquated) relative pronoun ―Welch‖ as a false friend
          of which tends to lead to a wrong preference of ―which‖ to ―who‖ by German speakers.
        3. Sub clauses/tenses: if clauses up to period/comma, giving the number of words as hints. Would
          require a delegate.
      6. Regular expressions in .Net have a number of advanced features that makes the platform a good choice for this enterprise: trpQuizGenerator-NLP-sample-German-personalpronomen-akk-dat
      7. The resulting texts can be e.g. easily delivered as formative assessment exercises  to students using trpQuiz.dot.
    2. Update:
      1. In a  much more recent approach to the same automation problem, I am trying to repurpose well-established existing NLP-platforms for question generation.
      2. However, compared with the above customized approach, to transform the built-in, not SLA-specific NLP recognition I have found so far taking not only much more work for reformatting for delivery, but also more creativity, or willingness to put up with limitations when it comes to homing in on typical learner problems.
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