@InProceedings{nguyen-verspoor:2018:BioNLP18,
  author    = {Nguyen, Dat Quoc  and  Verspoor, Karin},
  title     = {Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings},
  booktitle = {Proceedings of the BioNLP 2018 workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {129--136},
  abstract  = {We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model. We experiment with two common neural architectures, CNN and LSTM, to learn word vector representations from character embeddings. Through a task on the BioCreative-V CDR corpus, extracting relationships between chemicals and diseases, we show that models exploiting the character-based word representations improve on models that do not use this information, obtaining state-of-the-art result relative to previous neural approaches.},
  url       = {http://www.aclweb.org/anthology/W18-2314}
}