%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