@InProceedings{zhai-nguyen-verspoor:2018:LOUHI,
  author    = {Zhai, Zenan  and  Nguyen, Dat Quoc  and  Verspoor, Karin},
  title     = {Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition},
  booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {38--43},
  abstract  = {We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus show that the use of either type of character-level word embeddings in conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art performance. However, the models using CNN-based character-level word embeddings have a computational performance advantage, increasing training time over word-based models by 25% while the LSTM-based character-level word embeddings more than double the required training time.},
  url       = {http://www.aclweb.org/anthology/W18-5605}
}