Personal Information

Name

Dr. Ivan Habernal

Position
Postdoctoral Researcher
Affiliation
UKP-TUDA
E-Mail
habernal(a-t)ukp.informatik.tu-darmstadt.de
Office
S2|02 B107
Address

TU Darmstadt - FB 20
Hochschulstraße 10
64289 Darmstadt
Germany

Web

 

Google Scholar profile
GitHub profile

Research Interests

  • Computational Argumentation and Argumentation Mining
  • Natural Language Processing of User-Generated Content
  • Opinion Mining in Social Media

Biographical Information

Employment 

  • Since 09/2013: post-doctoral researcher at UKP Lab, Technical University Darmstadt, Germany
  • 10/2012 - 08/2013: research associate at Department of Computer Science and Engineering, University of West Bohemia, Plzen, Czech Republic

  • 07/2005 - 07/2006: J2EE developer at SoftEU, Pilsen, Czech Republic

Education

  • 2012: Ph.D. in Computer Science, University of West Bohemia, Czech Republic
    Thesis: "Semantic Web Search Using Natural Language"
  • 2007: MSc. in Computer Science, University of West Bohemia, Czech Republic
    Thesis: "Lexical Class Semantic Analysis

Professional Activities

Talks and resources

  • Recent Trends in Computational Argumentation. Invited talk, Zayed University, Dubai, October 2017
  • Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM, ACL 2016 long paper, Berlin, Germany, August 2016 (undefinedslides in PDF)
  • Existing Resources for Debating Technologies (joint talk with Christian Stab), Dagstuhl Seminar on Debating Technologies, Wadern, Germany, December 2015 (undefinedslides in PDF)
  • Detecting Argument Components and Structures (joint talk with Christian Stab), Dagstuhl Seminar on Debating Technologies, Wadern, Germany, December 2015 (undefinedslides in PDF)
  • A brief introduction to argument(ation) mining (talk held by Iryna Gurevych), Dagstuhl Seminar on Debating Technologies, Wadern, Germany, December 2015 (undefinedslides in PDF)
  • undefinedPoster in PDF presented at EMNLP 2015 for our article Exploiting Debate Portals for Semi-supervised Argumentation Mining in User-Generated Web Discourse, September 2015.
  • Machine learning for argumentation mining: Quick overview at the 2nd Workshop on Argumentation Mining, NAACL 2015, Denver, Colorado, June 2015 (undefinedslides in PDF)

Chair

Program committee member

Editor

Editorial board

Reviewer

Press coverage

Student supervision

  • Anil Narassiguin (2014, Internship, "Identification of Argumentative Texts in User-Generated Content on Educational Controversies")
  • Raffael Hannemann (2015, Master Thesis, "Serious Games for Large-Scale Argumentation Mining")
  • Christian Pollak (2015, Student Research Project)
  • Omnia Zayed (2015, Internship)
  • Dicle Öztürk (2015, Internship)
  • Christian Pollak (2016, Master Thesis)
  • Patrick Pauli (2017, Master Thesis)
  • Christopher Klamm (2017, Master Thesis)

Publications

The Argument Reasoning Comprehension Task

Author Ivan Habernal, Iryna Gurevych, Henning Wachsmuth, Benno Stein
Date August 2017
Kind Techreport
KeyTUD-CS-2017-0219
Research Areas UKP_a_ArMin, UKP_p_ArguAna, Ubiquitous Knowledge Processing
Abstract Reasoning is a crucial part of natural language argumentation. In order to comprehend an argument, one has to reconstruct and analyze its reasoning. As arguments are highly contextualized, most reasoning-related content is left implicit and usually presupposed. Thus, argument comprehension requires not only language understanding and logic skills, but it also heavily depends on common sense. In this article we define a new task, argument reasoning comprehension. Given a natural language argument with a reason and a claim, the goal is to choose the correct implicit reasoning from two options. The challenging factor is that both options are plausible and lexically very close while leading to contradicting claims. To provide an empirical common ground for the task, we propose a complex, yet scalable crowdsourcing process, and we create a new freely licensed dataset based on authentic arguments from news comments. While the resulting 2k high-quality instances are also suitable for other argumentation-related tasks, such as stance detection, argument component identification, and abstractive argument summarization, we focus ont the argument reasoning comprehension task and experiment with several systems based on neural attention or language models. Our results clearly reveal that current methods lack the capability to solve the task
Website https://github.com/UKPLab/argument-reasoning-comprehension-task
Full paper (pdf)
[Export this entry to BibTeX]

Important Copyright Notice:

The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
A A A | Drucken Print | Impressum Impressum | Sitemap Sitemap | Suche Search | Kontakt Contact | Webseitenanalyse: Mehr Informationen
zum Seitenanfangzum Seitenanfang