@InProceedings{barhaim-EtAl:2017:ArgumentMining,
  author    = {Bar-Haim, Roy  and  Edelstein, Lilach  and  Jochim, Charles  and  Slonim, Noam},
  title     = {Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {32--38},
  abstract  = {Stance classification is a core component in on-demand argument construction
	pipelines. Previous work on claim stance classification relied on background
	knowledge such as manually-composed sentiment lexicons. We show that both
	accuracy and coverage can be significantly improved through automatic expansion
	of the initial lexicon. We also developed a set of contextual features that
	further improves the state-of-the-art for this task.},
  url       = {http://www.aclweb.org/anthology/W17-5104}
}