Argument mining
Argument mining, or argumentation mining, is a research area within the
Applications
Argument mining has been applied in many different genres including the qualitative assessment of social media content (e.g. Twitter, Facebook), where it provides a powerful tool for policy-makers and researchers in social and political sciences.[1] Other domains include legal documents, product reviews, scientific articles, online debates, newspaper articles and dialogical domains. Transfer learning approaches have been successfully used to combine the different domains into a domain agnostic argumentation model.[5]
Argument mining has been used to provide students individual writing support by accessing and visualizing the argumentation discourse in their texts. The application of argument mining in a user-centered learning tool helped students to improve their argumentation skills significantly compared to traditional argumentation learning applications.[6]
Challenges
Given the wide variety of text genres and the different research perspectives and approaches, it has been difficult to reach a common and objective evaluation scheme.[7] Many annotated data sets have been proposed, with some gaining popularity, but a consensual data set is yet to be found. Annotating argumentative structures is a highly demanding task. There have been successful attempts to delegate such annotation tasks to the crowd but the process still requires a lot of effort and carries significant cost. Initial attempts to bypass this hurdle were made using the weak supervision approach.[8]
See also
- Argument technology – Sub-field of artificial intelligence
- Argumentation theory – Academic field of logic and rhetoric
- Logic translation – Translation of a text into a logical system
References
- ^ S2CID 9561587.
- ^ Budzynska, Katarzyna; Villata, Serena. "Argument Mining - IJCAI2016 Tutorial". www.i3s.unice.fr. Archived from the original on 2016-11-29. Retrieved 2018-03-30.
- ^ Gurevych, Iryna; Reed, Chris; Slonim, Noam; Stein, Benno. "NLP Approaches to Computational Argumentation - ACL 2016 Tutorial".
- ^ "5th Workshop on Argument Mining". 17 May 2011.
- ISBN 978-3-95545-335-0
- )
- S2CID 12346560.