@inproceedings{naderi-hirst-2017-recognizing,
title = "Recognizing Reputation Defence Strategies in Critical Political Exchanges",
author = "Naderi, Nona and
Hirst, Graeme",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_069",
doi = "10.26615/978-954-452-049-6_069",
pages = "527--535",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Recognizing Reputation Defence Strategies in Critical Political Exchanges
%A Naderi, Nona
%A Hirst, Graeme
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 sep
%I INCOMA Ltd.
%C Varna, Bulgaria
%F naderi-hirst-2017-recognizing
%X 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.
%R 10.26615/978-954-452-049-6_069
%U https://doi.org/10.26615/978-954-452-049-6_069
%P 527-535
Markdown (Informal)
[Recognizing Reputation Defence Strategies in Critical Political Exchanges](https://doi.org/10.26615/978-954-452-049-6_069) (Naderi & Hirst, RANLP 2017)
ACL