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

Argumentation Quality Assessment: Theory vs. Practice

Author Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych, Benno Stein
Date August 2017
Kind Inproceedings
PublisherAssociation for Computational Linguistics
Book titleProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)
Pages250-255
LocationVancouver, Canada
KeyTUD-CS-2017-0072
Research Areas UKP_a_ArMin, Ubiquitous Knowledge Processing
Abstract Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.
Full paper (pdf)
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