@InProceedings{fierro-EtAl:2017:ArgumentMining,
  author    = {Fierro, Constanza  and  Fuentes, Claudio  and  P\'{e}rez, Jorge  and  Quezada, Mauricio},
  title     = {200K+ Crowdsourced Political Arguments for a New Chilean Constitution},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-5101}
}