@InProceedings{ferrara-montanelli-petasis:2017:ArgumentMining,
  author    = {Ferrara, Alfio  and  Montanelli, Stefano  and  Petasis, Georgios},
  title     = {Unsupervised Detection of Argumentative Units though Topic Modeling Techniques},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {97--107},
  abstract  = {In this paper we present a new unsupervised approach, “Attraction to
	Topics” --
	A2T , for the detection of argumentative units, a sub-task of argument mining.
	Motivated by the importance of topic identification in manual annotation, we
	examine whether topic modeling can be used for performing unsupervised
	detection of argumentative sentences, and to what extend topic modeling can be
	used to classify sentences as claims and premises.
	Preliminary evaluation results suggest that topic information can be
	successfully used for the detection of argumentative sentences, at least for
	corpora used for evaluation.
	Our approach has been evaluated on two English corpora, the first of which
	contains 90 persuasive essays, while the second is a collection of 340
	documents from user generated content.},
  url       = {http://www.aclweb.org/anthology/W17-5113}
}