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
- Editors:
- Yang Liu, Tim Paek, Manasi Patwardhan
- 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
- Cite (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.
- Cite (Informal):
- ArgumenText: Searching for Arguments in Heterogeneous Sources (Stab et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/N18-5005.pdf