@InProceedings{barhaim-EtAl:2017:EACLlong,
  author    = {Bar-Haim, Roy  and  Bhattacharya, Indrajit  and  Dinuzzo, Francesco  and  Saha, Amrita  and  Slonim, Noam},
  title     = {Stance Classification of Context-Dependent Claims},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {251--261},
  abstract  = {Recent work has addressed the problem of detecting relevant claims for a given
	controversial topic. We introduce the complementary task of Claim Stance
	Classification, along with the first benchmark dataset for this task. We
	decompose this problem into: (a) open-domain target identification for topic
	and claim (b) sentiment classification for each target, and (c) open-domain
	contrast detection between the topic and the claim targets. Manual annotation
	of the dataset confirms the applicability and validity of our model. We
	describe an implementation of our model, focusing on a novel algorithm for
	contrast detection. Our approach achieves promising results, and is shown to
	outperform several baselines, which represent the common practice of applying a
	single, monolithic classifier for stance classification.},
  url       = {http://www.aclweb.org/anthology/E17-1024}
}