Cross-Domain Mining of Argumentative Text through Distant Supervision
- authored by
- Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Jonas Köhler, Benno Stein
- Abstract
Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applications, argumentative text segments have to be mined from large and diverse document collections. However, most existing argumentation mining approaches tackle the classification of argumentativeness only for a few manually annotated documents from narrow domains and registers. This limits their practical applicability. We hence propose a distant supervision approach that acquires argumentative text segments automatically from online debate portals. Experiments across domains and registers show that training on such a corpus improves the effectiveness and robustness of mining argumentative text. We freely provide the underlying corpus for research.
- External Organisation(s)
-
Bauhaus-Universität Weimar
- Type
- Conference contribution
- Pages
- 1395-1404
- No. of pages
- 10
- Publication date
- 06.2016
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Computer Science Applications, Linguistics and Language, Language and Linguistics
- Electronic version(s)
-
https://doi.org/10.18653/v1/n16-1165 (Access:
Open)
