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Computational Argumentation

Course, master, summer 2021, L.079.05811



  • Instructor. Henning Wachsmuth
  • Location. Tentatively entirely online (otherwise, in F1.110)
  • First date. April 15, 2021
  • Last date. July 22, 2021
  • Time. Thursday, 1pm – 4pm c.t. (live chat, weekly except for holidays)


  • Instructor. Milad Alshomary, Maximilian Spliethöver
  • Location. Tentatively entirely online (otherwise, in F1.110)
  • First date. April 21, 2021
  • Last date. July 21, 2021
  • Time. Wednesday, 11am – 1pm c.t. (live chat, in some weeks)


  • This edtion of the course ended. For the latest version of the slides, see the 2022 edition of the course.


Argumentation is an integral part of both professional and everyday communication. Whenever a topic or question is subject to controversy, people consider arguments to form opinions, to make decisions, or to convince others of a certain stance. In the last years, the computational analysis and synthesis of natural language argumentation has become an emerging research area, due to its importance for the next generation of web search engines and intelligent personal assistance. Based on fundamental techniques from natural language processing, computational argumentation ranges from the mining of arguments from natural language text, over the assessment of argumentation quality, to the retrieval of arguments in web search. 

The students learn both fundamentals from argumentation theory and state-of-the-art techniques from computational argumentation. The use of respective methods is practiced in assignments.

Lectures and Slides

The course covered lectures on the following topics. 

  1. Introduction to computational argumentation

  2. Basics of natural language processing

  3. Basics of argumentation

  4. Argument acquisition

  5. Argument mining

  6. Argument assessment

  7. Argument generation

  8. Applications of computational argumentation

  9. Conclusion (slides)

Voluntary Self-assessment

Under the following URL, you'll find a self-assessment test, which is designed to help decide whether this course is the right choice for you or not: 

The test covers a selection of basics from linguistics, statistics, and machine learning, all of which are considered as prerequisites for the course (along with good programming skills).

The test is voluntary, fully anonymous, and will not affect admission to the course. It is only intended for one's own consideration. However, students that struggle with several questions are advised to expand their skills first and maybe rather register next year. 


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