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

Argumentation Mining: Eine neue Methode zur automatisierten Textanalyse und ihre Anwendung in der Kommunikationswissenschaft

Markus Maurer, Johannes Daxenberger, Iryna Gurevych
In: Jahrestagung der Fachgruppe Methoden der Publizistik- und Kommunikationswissenschaft der Deutschen Gesellschaft für Publizistik- und Kommunikationswissenschaft, September 2017
[Inproceedings]

What is the Essence of a Claim? Cross-Domain Claim Identification

Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 2045-2056, September 2017
[Inproceedings]

UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection

Ji-Ung Lee, Steffen Eger, Johannes Daxenberger, Iryna Gurevych
In: Proceedings of the GSCL GermEval Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, p. (to appear), September 2017
[Inproceedings]

Training Argumentation Skills with Argumentative Writing Support

Christian Stab, Iryna Gurevych
In: Proceedings of the 21st Workshop on the Semantics and Pragmatics of Dialogue, p. 174--175, August 2017
[Online-Edition: http://www.saardial.uni-saarland.de/wordpress/wp-content/uploads/SemDial2017SaarDial_proceedings.pdf]
[Inproceedings]

The Argument Reasoning Comprehension Task

Ivan Habernal, Iryna Gurevych, Henning Wachsmuth, Benno Stein
August 2017
[Online-Edition: https://github.com/UKPLab/argument-reasoning-comprehension-task]
[Techreport]

Argumentation Quality Assessment: Theory vs. Practice

Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, Benno Stein
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 2: Short Papers, p. 250-255, August 2017
Association for Computational Linguistics
[Inproceedings]

Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (ACM TOIT)

Iryna Gurevych, Marco Lippi, Paolo Torroni
Vol. 17, August 2017
Association for Computing Machinery
[Book]

Neural End-to-End Learning for Computational Argumentation Mining

Steffen Eger, Johannes Daxenberger, Iryna Gurevych
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vol. Volume 1: Long Papers, p. 11-22, July 2017
Association for Computational Linguistics
[Inproceedings]

Recognizing Insufficiently Supported Arguments in Argumentative Essays

Christian Stab, Iryna Gurevych
In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), p. 980-990, April 2017
Association for Computational Linguistics
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/insufficiently-supported-arguments]
[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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