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

  • 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

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

Author Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, Iryna Gurevych
Date September 2017
Kind Inproceedings
Book titleProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Pages2045-2056
KeyTUD-CS-2017-0099
Research Areas Ubiquitous Knowledge Processing, UKP_a_ArMin, UKP_s_DKPro_TC
Abstract Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.
Full paper (pdf)
Slides
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