Reading 2: Argument Mining
Reading Theme
Argument Mining is the research area aiming at extracting natural language arguments and their relations from text, with the final goal of providing machine-processable structured data for computational models of argument. This research topic has started to attract the attention of a small community of researchers around 2014, and it is nowadays counted as one of the most promising research areas in AI in terms of growing of the community, funded projects, and involvement of companies.
Objective
To develop research thinking in the field of Argument Mining by reading the following book together:
Stede, M., & Schneider, J. (2018). Argumentation Mining: Synthesis Lectures on Human Language Technologies. Morgan and Claypool.
Some Other Useful References
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Survey track. Pages 5427-5433. https://www.ijcai.org/proceedings/2018/766
Argument Mining: A Survey (Lawrence & Reed, CL 2019)
Information
Date: July 5 (Wed.), July 12 (Wed.), July 19 (Wed.)
Time: 10:00 - 12:00
Place: Seminar Room 7F & WebEx Meeting
All speakers should share your presentation via this shared folder beforehand.
Each presentation should take about 40 minutes including the Q&A session.
Outline
Day 1 (July 5, 2023)
Chapter 1: Introduction (Speaker: Nitit Ngamphotchanamongkol)
Chapter 2: Argumentative Language (Speaker: Nitit Ngamphotchanamongkol)
Chapter 3: Modeling Arguments (Speaker: Surawat Pothong)
Day 2 (July 12, 2023)
Chapter 4: Corpus Annotation (Speaker: Surawat Pothong)
Chapter 5: Finding Claims (Speaker: Wuttichai Vijitkunsawat)
Chapter 6: Finding Supporting and Objecting Statements (Speaker: Wuttichai Vijitkunsawat)
Day 3 (July 19, 2023)
Chapter 7: Deriving the Structure of Argumentation (Speaker: Teeradaj Racharak)
Chapter 8: Assessing Argumentation (Speaker: Prabhat Parajuli)
Chapter 9: Generating Argumentative Text (Speaker: Prabhat Parajuli)
That is, we leave Chapter 10 (Summary and Perspectives) as a reading assignment for themselves.