@InProceedings{guggilla-miller-gurevych:2016:COLING,
  author    = {Guggilla, Chinnappa  and  Miller, Tristan  and  Gurevych, Iryna},
  title     = {CNN- and LSTM-based Claim Classification in Online User Comments},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2740--2751},
  abstract  = {When processing arguments in online user interactive discourse, it is often
	necessary to determine their bases of support. In this paper, we describe a
	supervised approach, based on deep neural networks, for classifying the claims
	made in online arguments. We conduct experiments using convolutional neural
	networks (CNNs) and long short-term memory networks (LSTMs) on two claim data
	sets compiled from online user comments. Using different types of
	distributional word embeddings, but without incorporating any rich, expensive
	set of features, we achieve a significant improvement over the state of the art
	for one data set (which categorizes arguments as factual vs. emotional), and
	performance comparable to the state of the art on the other data set (which
	categorizes propositions according to their verifiability). Our approach has
	the advantages of using a generalized, simple, and effective methodology that
	works for claim categorization on different data sets and tasks.},
  url       = {http://aclweb.org/anthology/C16-1258}
}