Argument mining

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]

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, on-line debates, newspaper articles and dialogical domains.

An argumentative search engine is a search engine that is given a controversial topic as a user query and returns a list of arguments for and against the topic.[5] Such an engine could be used to support informed decision making or help debaters prepare for a debate.

The goal of an automatic student essay scoring system is to assist students in improving their writing skills by measuring the quality of their argumentative content.[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

References

  1. 1 2 Lippi, Marco; Torroni, Paolo (2016-04-20). "Argumentation Mining: State of the Art and Emerging Trends". ACM Transactions on Internet Technology (TOIT). 16 (2): 10. doi:10.1145/2850417. ISSN 1533-5399.
  2. ↑ Budzynska, Katarzyna; Villata, Serena. "Argument Mining - IJCAI2016 Tutorial". www.i3s.unice.fr. 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".
  5. ↑ Aharoni, Ehud; et al. (2014). "Claims on demand–an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora". Proceedings of COLING 2014.
  6. ↑ Stab, Christian; Gurevych, Iryna (2014). "Identifying argumentative discourse structures in persuasive essays". Proceedings of EMNLP 2014.
  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.
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