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.[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.[2][3] The Argument Mining workshop series is the main research forum for argument mining related research.[4]


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]


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


  1. ^ a b 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". 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.