Overview

Argumentation is omnipresent in our daily communication and an important part of each decision making process. The recent research field of Argumentation Mining aims at automatically recognizing argumentation structures in written discourse in order to establish new intelligent systems for facilitating information access, writing skills acquisition and text summarization. This research area includes the following objectives:

  • Identifying argument components in different text types

  • Recognizing relations between argument components

  • Automatic assessment of argumentation quality

The research at UKP focuses on analyzing argumentation structures in written discourse. Our recent work is concerned with the analysis of argumentation structures in user-generated Web content (Habernal & Gurevych, 2015), scientific articles (Kirschner et al., 2015), and student texts (Stab & Gurevych, 2014).

Current Projects

  • Argumentative Writing Support (AWS): The goal of this project is to develop a novel writing assistance system in order to support authors in writing persuasive arguments and to improve their writing skills.

  • Large-scale argumentation mining on the Web: We aim at analyzing argumentation in various types of user-generated Web content, such as comments to articles, discussion forums, or blogs with the goal to overcome the current information overload and support users in decision-making.

  • Knowledge extraction and consolidation: This project focuses on the analysis of argumentation structures in scientific publications on a fine-grained level. The goal is to reveal how an author connects her thoughts in order to create a convincing line of argumentation. Such a fine-grained analysis of the argumentation structure will enable new ways information access, and could be integrated, for example, in summarization or faceted search applications as part of digital libraries.

  • ArguAna: Argumentation mining deals with the automatic identification of arguments and their relations from natural language text. This research project targets at the specific challenges of argumentation mining for the web. We seek to establish foundations of algorithms that apply argument mining to various forms of web argumentation, efficiently leverage the scale of the web, and complement argumentation mining with an argumentation analysis to effectively assess important quality dimensions.

Events

Resources

  • Argument Annotated Essays: A corpus of persuasive essays annotated with argumentation structures.
  • Argument Annotated Essays (version 2): An extended corpus of persuasive essays annotated with argumentation structures.
  • Argument Annotated User-Generated Web Discourse: A corpus contains user comments, forum posts, blogs and newspaper articles annotated with argument scheme based on extended Toulmin's model
  • Argument Annotated News Articles: A corpus of German documents on controversial educational topics (crawled from the Web, ca. 80% news articles) annotated with arguments according to the claim-premises scheme.
  • Argument Annotated Scientific Articles: A corpus of German scientific articles from the field of educational research, annotated with graph-structures of argumentative relations.
  • UKPConvArg1 Corpus: A corpus of 16k pairs of arguments for studying convincingness of Web arguments, as presented in our ACL 2016 paper.
  • UKPConvArg2 Corpus: A crowd-sourced corpus containing 9,111 argument pairs, multi-labeled with 17 classes, which was cleaned and curated by employing several strict quality measures. We proposed two tasks on this data set in our EMNLP 2016 paper, namely predicting the full label distribution and  classifying types of flaws in less convincing arguments.
  • Opposing Arguments: A corpus of 402 persuasive essays annotated with myside biases.
  • Insufficiently Supported Arguments: A corpus of 1,029 arguments annotated with the sufficiency criterion.

Software

Reference publications

Additional Attributes

Type

Existing Resources for Debating Technologies

Christian Stab, Ivan Habernal
In: Report of Dagstuhl Seminar on Debating Technologies (15512), Vol. 5, p. 32-33, 2016
[Online-Edition: http://www.dagstuhl.de/15512]
[Article]

Exploiting Debate Portals for Semi-supervised Argumentation Mining in User-Generated Web Discourse

Ivan Habernal, Iryna Gurevych
In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 2127--2137, September 2015
Association for Computational Linguistics
[Online-Edition: https://github.com/UKPLab/emnlp2015]
[Inproceedings]

On the Role of Discourse Markers for Discriminating Claims and Premises in Argumentative Discourse

Judith Eckle-Kohler, Roland Kluge, Iryna Gurevych
In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 2249-2255, September 2015
Association for Computational Linguistics
[Online-Edition: https://www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-news-articles/ - - Downloads]
[Inproceedings]

Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications

Christian Kirschner, Judith Eckle-Kohler, Iryna Gurevych
In: Proceedings of the 2nd Workshop on Argumentation Mining held in conjunction with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), p. 1-11, June 2015
[Online-Edition: https://www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-scientific-articles/]
[Inproceedings]

Proceedings of the 2nd Workshop on Argumentation Mining

Claire Cardie, Nancy Green, Iryna Gurevych, Diane Litman, Smaranda Muresan, Georgios Petasis, Manfred Stede, Marilyn Walker, Janyce Wiebe
June 2015
Association for Computational Linguistics
[Online-Edition: https://www.cs.cornell.edu/home/cardie/naacl-2nd-arg-mining/]
[Proceedings]

Serious Games for large-scale Argumentation Mining

Raffael Hannemann
April 2015
[Thesis (Master, Bachelor, Diploma)]

Identifying Argumentative Discourse Structures in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), p. 46-56, October 2014
Association for Computational Linguistics
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-essays/]
[Inproceedings]

Annotating Argument Components and Relations in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), p. 1501-1510, August 2014
Dublin City University and Association for Computational Linguistics
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-essays/]
[Inproceedings]

Argumentation Mining in Persuasive Essays and Scientific Articles from the Discourse Structure Perspective

Christian Stab, Christian Kirschner, Judith Eckle-Kohler, Iryna Gurevych
In: Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing, p. 40--49, July 2014
CEUR-WS
[Online-Edition: http://ceur-ws.org/Vol-1341/]
[Inproceedings]

Argumentation Mining on the Web from Information Seeking Perspective

Ivan Habernal, Judith Eckle-Kohler, Iryna Gurevych
In: Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing, p. 26--39, July 2014
CEUR-WS
[Online-Edition: http://ceur-ws.org/Vol-1341/]
[Inproceedings]

Cooperation partners

  • Educational Testing Service (NLP division)

  • Prof. Fischer (network of excellence)

  • Prof. Dr. Benno Stein

  • Macmillan Science and Education

Primary Contact

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