@InProceedings{levy-EtAl:2017:ArgumentMining,
  author    = {Levy, Ran  and  Gretz, Shai  and  Sznajder, Benjamin  and  Hummel, Shay  and  Aharonov, Ranit  and  Slonim, Noam},
  title     = {Unsupervised corpus--wide claim detection},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {79--84},
  abstract  = {Automatic claim detection is a fundamental
	argument mining task that aims to automatically
	mine claims regarding a topic
	of consideration. Previous works on mining
	argumentative content have assumed
	that a set of relevant documents is given in
	advance. Here, we present a first corpus--
	wide claim detection framework, that can
	be directly applied to massive corpora.
	Using simple and intuitive empirical observations,
	we derive a claim sentence
	query by which we are able to directly retrieve
	sentences in which the prior probability
	to include topic-relevant claims is
	greatly enhanced. Next, we employ simple
	heuristics to rank the sentences, leading
	to an unsupervised corpus--wide claim detection
	system, with precision that outperforms
	previously reported results on the
	task of claim detection given relevant documents
	and labeled data.},
  url       = {http://www.aclweb.org/anthology/W17-5110}
}