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Argument mining, or 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.cite-ref-lippi2016-1-0[1] 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.cite-ref-ijcai-tutorial-2-0[2]cite-ref-acl-tutorial-3-0[3] The Argument Mining workshop series is the main research forum for argument mining related research.cite-ref-argmining2018-4-0[4]

• Applications

• Challenges

• See also

• References

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.cite-ref-lippi2016-1-1[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.cite-ref-5[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.cite-ref-6[6]

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.cite-ref-unshared-task-7-0[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.cite-ref-levy2017-8-0[8]

• 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

cite-note-lippi2016-11. ↑ citereflippitorroni2016Lippi, 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.

cite-note-ijcai-tutorial-22. ↑ citerefbudzynskavillataBudzynska, Katarzyna; Villata, Serena. "Argument Mining - IJCAI2016 Tutorial". www.i3s.unice.fr. Archived from the original on 2016-11-29. Retrieved 2018-03-30.

cite-note-acl-tutorial-33. ↑ citerefgurevychreedslonimsteinGurevych, Iryna; Reed, Chris; Slonim, Noam; Stein, Benno. "NLP Approaches to Computational Argumentation - ACL 2016 Tutorial".

cite-note-argmining2018-44. ↑ "5th Workshop on Argument Mining". 17 May 2011.

cite-note-55. ↑ citerefwambsganssmolyndriss-llner2020Wambsganss, 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

cite-note-66. ↑ "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)

cite-note-unshared-task-77. ↑ "Unshared Task - 3rd Workshop on Argument Mining".

cite-note-levy2017-88. ↑ citereflevygretzsznajderhummel2017Levy, 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.