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.
@inproceedings{
stab2018argumentext,
author = {Christian Stab and Johannes Daxenberger and Chris
Stahlhut and Tristan Miller and Benjamin Schiller and Christopher Tauchmann
and Steffen Eger and Iryna Gurevych},
title = {{ArgumenText}: Searching for Arguments in
Heterogeneous Sources},
booktitle = {Proceedings of the 16th {Annual} {Conference} of the
{North} {American} {Chapter} of the {Association} for {Computational}
{Linguistics}: Human Language Technologies: Demonstrations ({NAACL}-{HLT}
2018)},
pages = {21--25},
month = jun,
year = {2018},
isbn = {978-1-948087-28-5},
doi = {10.18653/v1/N18-5005},
}