Gamification of Video Annotations

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About Gamification of Video Annotations

  • This project has not yet been fulfilled.
  • This project fits in the following Bachelor programs: {{#arraymap:|, |xXx|bachelorproject within::xXx|,}}
  • This project fits in the following masterareas: {{#arraymap:Information and Communication Technology, Multimedia, Internet and Web Technology, AI and Communication, Technical Artificial Intelligence, Human Ambience, Cognitive Science, Knowledge Technology and Intelligent Internet Applications, Computer Science and Communication, Information Sciences|, |xXx|project within::xXx|,}}
  • Project website: has projectpage:: http://crowdtruth.org

Description

This assignment will be done in the context of the CrowdTruth project for crowdsourcing and gaming strategies in annotating text, images and videos.

Intelligent Systems typically need annotated data to be trained to understand

answer questions posed in natural language. For this they need to trained in understanding texts in natural language. Currently, Watson is being trained in medical domain, and one of the challenges is to learn how to EXTRACT IMPORTANT MEDICAL TERMS FROM PATIENT HISTORY TEXTS.

In this MSc project you will explore the use of crowdsourcing and/or games with purpose for the annotation of such texts with the important terms and their relations.

The work on this Master's project is performed in collaboration with IBM Research, Chris Welty one of the developers of Watson, the IBM computer that defeated the best players on the American game show Jeopardy!.

Tasks

  • explore the domain of serious games and games with purpose
  • model the patient history (medical diagnosis) scenario
  • identify gaming elements that are appropriate for this scenario
  • define game scenarios
  • evaluate and compare the game scenarios

Tools and Data

In this project you will be working with the following data:

  • patient history texts, e.g. New England Journal of Medicine
  • types of terms and relations, e.g. observation, finding, diagnosis, treatment
  • examples of games with purpose, e.g. DuoLingo
  • examples of active learning mechanisms, e.g. iPhoto face recognition feedback cycle

Recommended prior knowledge

  • Knowledge & Media course
  • Social Web course
  • Research methods course
  • Serious Games
  • Note: no medical knowledge is required for this assignment

Extra Information

Contact Lora_Aroyo, Michiel_Hildebrand or Guus_Schreiber for more information about this project.