@inproceedings{delobelle-etal-2019-computational,
title = "Computational Ad Hominem Detection",
author = "Delobelle, Pieter and
Cunha, Murilo and
Massip Cano, Eric and
Peperkamp, Jeroen and
Berendt, Bettina",
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2028",
doi = "10.18653/v1/P19-2028",
pages = "203--209",
abstract = "Fallacies like the personal attack{---}also known as the ad hominem attack{---}are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80{\%} for a social media data source. We also demonstrate our approach with an application that uses online learning.",
}
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<abstract>Fallacies like the personal attack—also known as the ad hominem attack—are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80% for a social media data source. We also demonstrate our approach with an application that uses online learning.</abstract>
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%0 Conference Proceedings
%T Computational Ad Hominem Detection
%A Delobelle, Pieter
%A Cunha, Murilo
%A Massip Cano, Eric
%A Peperkamp, Jeroen
%A Berendt, Bettina
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F delobelle-etal-2019-computational
%X Fallacies like the personal attack—also known as the ad hominem attack—are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80% for a social media data source. We also demonstrate our approach with an application that uses online learning.
%R 10.18653/v1/P19-2028
%U https://aclanthology.org/P19-2028
%U https://doi.org/10.18653/v1/P19-2028
%P 203-209
Markdown (Informal)
[Computational Ad Hominem Detection](https://aclanthology.org/P19-2028) (Delobelle et al., ACL 2019)
ACL
- Pieter Delobelle, Murilo Cunha, Eric Massip Cano, Jeroen Peperkamp, and Bettina Berendt. 2019. Computational Ad Hominem Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 203–209, Florence, Italy. Association for Computational Linguistics.