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

Argumentation Mining in User-Generated Web Discourse

Author Ivan Habernal, Iryna Gurevych
Date 2017
Kind Article
JournalComputational Linguistics
Number1
Pages125-179
DOI10.1162/COLI_a_00276
KeyTUD-CS-2016-0013
Research Areas Ubiquitous Knowledge Processing, UKP_a_ArMin
Abstract The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.
Website http://dx.doi.org/10.1162/COLI_a_00276
Full paper (pdf)
[Export this entry to BibTeX]

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