ArgumenText: Searching for Arguments in Heterogeneous Sources

Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych


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.
Anthology ID:
N18-5005
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–25
Language:
URL:
https://aclanthology.org/N18-5005
DOI:
10.18653/v1/N18-5005
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N18-5005.pdf