@InProceedings{chalaguine-schulz:2017:EACLSRW17,
  author    = {Chalaguine, Lisa Andreevna  and  Schulz, Claudia},
  title     = {Assessing Convincingness of Arguments in Online Debates with Limited Number of Features},
  booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {75--83},
  abstract  = {We propose a new method in the field of argument analysis in social media to
	determining convincingness of arguments in online debates, following previous
	research by Habernal and Gurevych (2016). Rather than using argument specific
	feature values, we measure feature values relative to the average value in the
	debate, allowing us to determine argument convincingness with fewer features
	(between 5 and 35) than normally used for natural language processing tasks. We
	use a simple forward-feeding neural network for this task and achieve an
	accuracy of 0.77 which is comparable to the accuracy obtained using 64k
	features and a support vector machine by Habernal and Gurevych.},
  url       = {http://www.aclweb.org/anthology/E17-4008}
}