@InProceedings{hua-wang:2017:Short,
  author    = {Hua, Xinyu  and  Wang, Lu},
  title     = {Understanding and Detecting Supporting Arguments of Diverse Types},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {203--208},
  abstract  = {We investigate the problem of sentence-level supporting argument detection from
	relevant documents for user-specified claims. A dataset containing claims and
	associated citation articles is collected from online debate website
	idebate.org. We then manually label sentence-level supporting arguments from
	the documents along with their types as study, factual, opinion, or reasoning.
	We further characterize arguments of different types, and explore whether
	leveraging type information can facilitate the supporting arguments detection
	task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses
	features informed by argument types yields better performance than the same
	ranker trained without type information.},
  url       = {http://aclweb.org/anthology/P17-2032}
}