@inproceedings{stab-etal-2018-argumentext,
title = "{A}rgumen{T}ext: Searching for Arguments in Heterogeneous Sources",
author = "Stab, Christian and
Daxenberger, Johannes and
Stahlhut, Chris and
Miller, Tristan and
Schiller, Benjamin and
Tauchmann, Christopher and
Eger, Steffen and
Gurevych, Iryna",
editor = "Liu, Yang and
Paek, Tim and
Patwardhan, Manasi",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-5005",
doi = "10.18653/v1/N18-5005",
pages = "21--25",
abstract = "Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89{\%} of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.",
}
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<abstract>Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.</abstract>
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%0 Conference Proceedings
%T ArgumenText: Searching for Arguments in Heterogeneous Sources
%A Stab, Christian
%A Daxenberger, Johannes
%A Stahlhut, Chris
%A Miller, Tristan
%A Schiller, Benjamin
%A Tauchmann, Christopher
%A Eger, Steffen
%A Gurevych, Iryna
%Y Liu, Yang
%Y Paek, Tim
%Y Patwardhan, Manasi
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F stab-etal-2018-argumentext
%X Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
%R 10.18653/v1/N18-5005
%U https://aclanthology.org/N18-5005
%U https://doi.org/10.18653/v1/N18-5005
%P 21-25
Markdown (Informal)
[ArgumenText: Searching for Arguments in Heterogeneous Sources](https://aclanthology.org/N18-5005) (Stab et al., NAACL 2018)
ACL
- Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, and Iryna Gurevych. 2018. ArgumenText: Searching for Arguments in Heterogeneous Sources. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 21–25, New Orleans, Louisiana. Association for Computational Linguistics.