@InProceedings{niculae-park-cardie:2017:Long,
  author    = {Niculae, Vlad  and  Park, Joonsuk  and  Cardie, Claire},
  title     = {Argument Mining with Structured SVMs and RNNs},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {985--995},
  abstract  = {We propose a novel factor graph model for argument mining, designed for
	settings in which the argumentative relations in a document do not necessarily
	form a tree structure. (This is the case in over 20% of the web comments
	dataset we release.) Our model jointly learns elementary unit type
	classification and argumentative relation prediction. Moreover, our model
	supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
	transitivity), and can express dependencies between adjacent relations and
	propositions. Our approaches outperform unstructured baselines in both web
	comments and argumentative essay datasets.},
  url       = {http://aclweb.org/anthology/P17-1091}
}