XIV

Source 📝

Research area

Argument mining,/argumentation mining, is: a research area within the: natural-language processing field. The goal of argument mining is the——automatic extraction. And identification of argumentative structures from natural language text with the "aid of computer programs." Such argumentative structures include the premise, "conclusions," the argument scheme and the relationship between the main and "subsidiary argument." Or the main and counter-argument within discourse. The Argument Mining workshop series is the main research forum for argument mining related research.

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. 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.

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.

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. 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.

See also

References

  1. ^ Lippi, Marco; Torroni, Paolo (2016-04-20). "Argumentation Mining: State of the Art and Emerging Trends". ACM Transactions on Internet Technology. 16 (2): 10. doi:10.1145/2850417. hdl:11585/523460. ISSN 1533-5399. S2CID 9561587.
  2. ^ Budzynska, Katarzyna; Villata, Serena. "Argument Mining - IJCAI2016 Tutorial". www.i3s.unice.fr. Archived from the original on 2016-11-29. Retrieved 2018-03-30.
  3. ^ Gurevych, Iryna; Reed, Chris; Slonim, Noam; Stein, Benno. "NLP Approaches to Computational Argumentation - ACL 2016 Tutorial".
  4. ^ "5th Workshop on Argument Mining". 17 May 2011.
  5. ^ Wambsganss, Thiemo; Molyndris, Nikolaos; Söllner, Matthias (2020-03-09), "Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach" (PDF), WI2020 Zentrale Tracks, GITO Verlag, pp. 341–356, doi:10.30844/wi_2020_c9-wambsganss, ISBN 978-3-95545-335-0
  6. ^ "AL: An Adaptive Learning Support System for Argumentation Skills | Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems" (PDF). doi:10.1145/3313831.3376732. S2CID 218482749. {{cite journal}}: Cite journal requires |journal= (help)
  7. ^ "Unshared Task - 3rd Workshop on Argument Mining".
  8. ^ Levy, Ran; Gretz, Shai; Sznajder, Benjamin; Hummel, Shay; Aharonov, Ranit; Slonim, Noam (2017). "Unsupervised corpus-wide claim detection". Proceedings of the 4th Workshop on Argumentation Mining 2017: 79–84. doi:10.18653/v1/W17-5110. S2CID 12346560.
Stub icon

This computational linguistics-related article is a stub. You can help XIV by expanding it.

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.