@inproceedings{lawrence-reed-2017-using,
title = "Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates",
author = "Lawrence, John and
Reed, Chris",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5114",
doi = "10.18653/v1/W17-5114",
pages = "108--117",
abstract = "In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.",
}
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%0 Conference Proceedings
%T Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates
%A Lawrence, John
%A Reed, Chris
%Y Habernal, Ivan
%Y Gurevych, Iryna
%Y Ashley, Kevin
%Y Cardie, Claire
%Y Green, Nancy
%Y Litman, Diane
%Y Petasis, Georgios
%Y Reed, Chris
%Y Slonim, Noam
%Y Walker, Vern
%S Proceedings of the 4th Workshop on Argument Mining
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lawrence-reed-2017-using
%X In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.
%R 10.18653/v1/W17-5114
%U https://aclanthology.org/W17-5114
%U https://doi.org/10.18653/v1/W17-5114
%P 108-117
Markdown (Informal)
[Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates](https://aclanthology.org/W17-5114) (Lawrence & Reed, ArgMining 2017)
ACL