@inproceedings{fierro-etal-2017-200k,
title = "200{K}+ Crowdsourced Political Arguments for a New {C}hilean Constitution",
author = "Fierro, Constanza and
Fuentes, Claudio and
P{\'e}rez, Jorge and
Quezada, Mauricio",
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/W17-5101",
doi = "10.18653/v1/W17-5101",
pages = "1--10",
abstract = "In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90{\%} top-5 accuracy in a multi-class classification of arguments, and 65{\%} macro-averaged F1-score for tagging arguments according to a three-part argumentation model.",
}
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%0 Conference Proceedings
%T 200K+ Crowdsourced Political Arguments for a New Chilean Constitution
%A Fierro, Constanza
%A Fuentes, Claudio
%A Pérez, Jorge
%A Quezada, Mauricio
%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 fierro-etal-2017-200k
%X In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90% top-5 accuracy in a multi-class classification of arguments, and 65% macro-averaged F1-score for tagging arguments according to a three-part argumentation model.
%R 10.18653/v1/W17-5101
%U https://aclanthology.org/W17-5101
%U https://doi.org/10.18653/v1/W17-5101
%P 1-10
Markdown (Informal)
[200K+ Crowdsourced Political Arguments for a New Chilean Constitution](https://aclanthology.org/W17-5101) (Fierro et al., ArgMining 2017)
ACL