Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) |
| Editors | Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan |
| Pages | 2922-2932 |
| Number of pages | 11 |
| Publication status | Published - Nov 2019 |
| Externally published | Yes |
| Event | 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 - Hong Kong, China Duration: 3 Nov 2019 → 7 Nov 2019 |
Abstract
In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When discussing the legalization of drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author's stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience's interests and cultural background. This paper introduces frame identification, which is the task of splitting a set of arguments into a set of non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12 326 debate-portal arguments, organized along the frames of the debates' topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
ASJC Scopus subject areas
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Information Systems
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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ed. / Kentaro Inui; Jing Jiang; Vincent Ng; Xiaojun Wan. 2019. p. 2922-2932.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Modeling Frames in Argumentation
AU - Ajjour, Yamen
AU - Alshomary, Milad
AU - Wachsmuth, Henning
AU - Stein, Benno
PY - 2019/11
Y1 - 2019/11
N2 - In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When discussing the legalization of drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author's stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience's interests and cultural background. This paper introduces frame identification, which is the task of splitting a set of arguments into a set of non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12 326 debate-portal arguments, organized along the frames of the debates' topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
AB - In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When discussing the legalization of drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author's stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience's interests and cultural background. This paper introduces frame identification, which is the task of splitting a set of arguments into a set of non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12 326 debate-portal arguments, organized along the frames of the debates' topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
UR - http://www.scopus.com/inward/record.url?scp=85084291029&partnerID=8YFLogxK
U2 - 10.18653/v1/D19-1290
DO - 10.18653/v1/D19-1290
M3 - Conference contribution
AN - SCOPUS:85084291029
SN - 9781950737901
SP - 2922
EP - 2932
BT - Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
A2 - Inui, Kentaro
A2 - Jiang, Jing
A2 - Ng, Vincent
A2 - Wan, Xiaojun
T2 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019
Y2 - 3 November 2019 through 7 November 2019
ER -