@inproceedings{48302,
  author       = {{Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}},
  booktitle    = {{Proceedings of the 2018 Conference of the North American Chapter of          the Association for Computational Linguistics: Human Language          Technologies, Volume 1 (Long Papers)}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies            in Web Argumentation}}},
  doi          = {{10.18653/v1/n18-1036}},
  year         = {{2018}},
}

@inproceedings{48304,
  author       = {{Daxenberger, Johannes and Eger, Steffen and Habernal, Ivan and Stab, Christian and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 2017 Conference on Empirical Methods in Natural          Language Processing}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{What is the Essence of a Claim? Cross-Domain Claim Identification}}},
  doi          = {{10.18653/v1/d17-1218}},
  year         = {{2018}},
}

@inproceedings{48305,
  author       = {{Wachsmuth, Henning and Naderi, Nona and Habernal, Ivan and Hou, Yufang and Hirst, Graeme and Gurevych, Iryna and Stein, Benno}},
  booktitle    = {{Proceedings of the 55th Annual Meeting of the Association for          Computational Linguistics (Volume 2: Short Papers)}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Argumentation Quality Assessment: Theory vs. Practice}}},
  doi          = {{10.18653/v1/p17-2039}},
  year         = {{2017}},
}

@article{48306,
  abstract     = {{<jats:p>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.</jats:p>}},
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  issn         = {{0891-2017}},
  journal      = {{Computational Linguistics}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, Linguistics and Language, Language and Linguistics}},
  number       = {{1}},
  pages        = {{125--179}},
  publisher    = {{MIT Press}},
  title        = {{{Argumentation Mining in User-Generated Web Discourse}}},
  doi          = {{10.1162/coli_a_00276}},
  volume       = {{43}},
  year         = {{2016}},
}

@inproceedings{48308,
  author       = {{Habernal, Ivan and Sukhareva, Maria and Raiber, Fiana and Shtok, Anna and Kurland, Oren and Ronen, Hadar and Bar-Ilan, Judit and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval}},
  publisher    = {{ACM}},
  title        = {{{New Collection Announcement}}},
  doi          = {{10.1145/2911451.2914682}},
  year         = {{2016}},
}

@inproceedings{48307,
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM}}},
  doi          = {{10.18653/v1/p16-1150}},
  year         = {{2016}},
}

@inproceedings{48309,
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 2016 Conference on Empirical Methods in Natural          Language Processing}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{What makes a convincing argument? Empirical analysis and detecting            attributes of convincingness in Web argumentation}}},
  doi          = {{10.18653/v1/d16-1129}},
  year         = {{2016}},
}