@InProceedings{huynh-EtAl:2016:COLING,
  author    = {Huynh, Trung  and  He, Yulan  and  Willis, Alistair  and  Rueger, Stefan},
  title     = {Adverse Drug Reaction Classification With Deep Neural Networks},
  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     = {877--887},
  abstract  = {We study the problem of detecting sentences describing adverse drug reactions
	(ADRs) and frame the problem as binary classification. We investigate different
	neural network (NN) architectures for ADR classification. In particular, we
	propose two new neural network models, Convolutional Recurrent Neural Network
	(CRNN) by concatenating convolutional neural networks with recurrent neural
	networks, and Convolutional Neural Network with Attention (CNNA) by adding
	attention weights into convolutional neural networks. We evaluate various NN
	architectures on a Twitter dataset containing informal language and an Adverse
	Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports.
	Experimental results show that all the NN architectures outperform the
	traditional maximum entropy classifiers trained from n-grams with different
	weighting strategies considerably on both datasets. On the Twitter dataset, all
	the NN architectures perform similarly. But on the ADE dataset, CNN performs
	better than other more complex CNN variants. Nevertheless, CNNA allows the
	visualisation of attention weights of words when making classification
	decisions and hence is more appropriate for the extraction of word subsequences
	describing ADRs.},
  url       = {http://aclweb.org/anthology/C16-1084}
}