Computational Argumentation

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 assistants. Based on statistical natural language processing techniques, computational argumentation covers the mining of arguments from natural language text, the assessment of stance argument quality, as well as the generation of new claims and arguments. The students learn both fundamentals from argumentation theory and state-of-the-art methods from computational argumentation. Assignments deepen the understanding of the methods.

• Introduction to Computational Argumentation
• Basics of Natural Language Processing
• Basics of Argumentation
• Argument Mining
• Argument Assessment
• Argument Generation
• Applications of Computational Argumentation
• Conclusion

• Basics of statistics
• Knowledge of programming, ideally Python
• Any course on machine learning or artificial intelligence
• Master's course: Statistical Natural Language Processing (preferred)
• Bachelor's course: Introduction to Natural Language Processing (alternatively)

• Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. Prentice-Hall, 2nd edition. Free draft of third edition: Speech and Language Processing

• Manfred Stede and Jodi Schneider. 2018. Argumentation Mining. Synthesis Lectures on Human Language Technologies 40, Morgan & Claypool.

Lecture slides

• Part I – Introduction to Computational Argumentation (slides)
• Part II – Basics of Natural Language Processing (slides)
• Part III – Basics of Argumentation (slides from last year)
• Part IV – Argument Mining (slides from last year)
• Part V – Argument Assessment (slides from last year)
• Part VI – Argument Generation (slides from last year)
• Part VII – Applications of Computational Argumentation (slides from last year)
• Part VIII – Conclusion (slides from last year)

Organizational information

• Organizational course Information (slides​​​​​​​)