@InProceedings{peng-lu:2017:BioNLP17,
  author    = {Peng, Yifan  and  Lu, Zhiyong},
  title     = {Deep learning for extracting protein-protein interactions from biomedical literature},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {29--38},
  abstract  = {State-of-the-art methods for protein-protein interaction (PPI) extraction are
	primarily feature-based or kernel-based by leveraging lexical and syntactic
	information. But how to incorporate such knowledge in the recent deep learning
	methods remains an open question. In this paper, we propose a
	multichannel dependency-based convolutional neural network model (McDepCNN). It
	applies one channel to the embedding vector of each word in the sentence, and
	another channel to the embedding vector of the head of the corresponding word.
	Therefore, the model can use richer information obtained from different
	channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
	demonstrate that McDepCNN provides up to 6% F1-score improvement over rich
	feature-based methods and single-kernel methods. In addition, McDepCNN achieves
	24.4% relative improvement in F1-score over the state-of-the-art methods on
	cross-corpus evaluation and 12% improvement in F1-score over kernel-based
	methods on "difficult" instances. These results suggest that McDepCNN
	generalizes more easily over different corpora, and is capable of capturing
	long distance features in the sentences.},
  url       = {http://www.aclweb.org/anthology/W17-2304}
}