Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics |
| Subtitle of host publication | Human Language Technologies |
| Pages | 1395-1404 |
| Number of pages | 10 |
| ISBN (electronic) | 9781941643914 |
| Publication status | Published - Jun 2016 |
| Externally published | Yes |
| Event | 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - San Diego, United States Duration: 12 Jun 2016 → 17 Jun 2016 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Linguistics and Language
- Arts and Humanities(all)
- Language and Linguistics
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Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016. p. 1395-1404.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Cross-Domain Mining of Argumentative Text through Distant Supervision
AU - Al-Khatib, Khalid
AU - Wachsmuth, Henning
AU - Hagen, Matthias
AU - Köhler, Jonas
AU - Stein, Benno
PY - 2016/6
Y1 - 2016/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84994152079&partnerID=8YFLogxK
U2 - 10.18653/v1/n16-1165
DO - 10.18653/v1/n16-1165
M3 - Conference contribution
AN - SCOPUS:84994152079
SP - 1395
EP - 1404
BT - Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics
T2 - 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Y2 - 12 June 2016 through 17 June 2016
ER -