@InProceedings{hsieh-EtAl:2017:I17-22,
  author    = {Hsieh, Yu-Lun  and  Chang, Yung-Chun  and  Chang, Nai-Wen  and  Hsu, Wen-Lian},
  title     = {Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {240--245},
  abstract  = {In this paper, we propose a recurrent neural network model for identifying
	protein-protein interactions in biomedical literature. Experiments on two
	largest public benchmark datasets, AIMed and BioInfer, demonstrate that our
	approach significantly surpasses state-of-the-art methods with relative
	improvements of 10% and 18%, respectively. Cross-corpus evaluation also
	demonstrate that the proposed model remains robust despite using different
	training data. These results suggest that RNN can effectively capture semantic
	relationships among proteins as well as generalizes over different corpora,
	without any feature engineering.},
  url       = {http://www.aclweb.org/anthology/I17-2041}
}