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Using NLP to generate Quizzes


Vishal Gupta


The Problem

  • Quizzes/Questions are required by students for self-evaluation and by teachers to test students.
  • However, quiz creation currently requires manual effort and understanding of the topic which making the process difficult to adapt and scale.
  • Multiple-Choice Questions have been proved to evaluate student’s understanding of concepts and can exhibit broad ranges of difficulty and therefore, test different levels of understanding.
  • However, MCQs require challenging options which can be cumbersome to pick.

The Solution

  • Using NLP to generate Multiple-Choice Questions from plain-text
    • Statistically determines best entities to blank
    • Generate semantically similar options based on context and corpus
    • Better than subjective questions MC questions tests
  • A user-interface to generate quizzes
    • From any document
    • On any topic in any genre (Educational or Recreational)
    • From any paragraph


  • Inspiration
  • Problem Statement
  • How it’s done
    • Brief introduction to NLP and NER (Named Entity Recognition)
    • Using Named Entity Recognition to pick questions
    • Ranking potential questions
    • Picking relevant/similar options to accompany answer in MCQ
    • Deploying the solution
  • Scope and application of the solution

Speaker bio

  • Machine Learning Engineer at Freshworks since 2019. Work on building AI models for Freshsales, our Sales CRM.
  • Worked with a number of startups on NLP and ML problems.
  • Google Summer of Code‘18 under Debian