Recognizing Reputation Defence Strategies in Critical Political Exchanges

Nona Naderi, Graeme Hirst


Abstract
We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and reputation defence, we determine the reputation defence strategy. We annotate a dataset of parliamentary questions and answers with reputation defence strategies. We then propose a model based on supervised learning to address the detection of these strategies, and report promising experimental results.
Anthology ID:
R17-1069
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
527–535
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_069
DOI:
10.26615/978-954-452-049-6_069
Bibkey:
Cite (ACL):
Nona Naderi and Graeme Hirst. 2017. Recognizing Reputation Defence Strategies in Critical Political Exchanges. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 527–535, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Recognizing Reputation Defence Strategies in Critical Political Exchanges (Naderi & Hirst, RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_069