@InProceedings{asada-miwa-sasaki:2017:BioNLP17,
  author    = {Asada, Masaki  and  Miwa, Makoto  and  Sasaki, Yutaka},
  title     = {Extracting Drug-Drug Interactions with Attention CNNs},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {9--18},
  abstract  = {We propose a novel attention mechanism for a Convolutional Neural Network
	(CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown
	to have a great potential on DDI extraction tasks; however, attention
	mechanisms, which emphasize important words in the sentence of a target-entity
	pair, have not been investigated with the CNNs despite the fact that attention
	mechanisms are shown to be effective for a general domain relation
	classification task. We evaluated our model on the Task 9.2 of the
	DDIExtraction-2013 shared task. As a result, our attention mechanism improved
	the performance of our base CNN-based DDI model, and the model achieved an
	F-score of 69.12%, which is competitive with the state-of-the-art models.},
  url       = {http://www.aclweb.org/anthology/W17-2302}
}