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

Recognizing Insufficiently Supported Arguments in Argumentative Essays

Author Christian Stab, Iryna Gurevych
Date April 2017
Kind Inproceedings
PublisherAssociation for Computational Linguistics
Book titleProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017)
Pages980-990
LocationValencia, Spain
KeyTUD-CS-2017-0010
Research Areas Ubiquitous Knowledge Processing, UKP_reviewed, UKP_a_ArMin
Abstract In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality.
Website www.ukp.tu-darmstadt.de/data/argumentation-mining/insufficiently-supported-arguments
Full paper (pdf)
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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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