@inproceedings{zhai-etal-2018-comparing,
    title = "Comparing {CNN} and {LSTM} character-level embeddings in {B}i{LSTM}-{CRF} models for chemical and disease named entity recognition",
    author = "Zhai, Zenan  and
      Nguyen, Dat Quoc  and
      Verspoor, Karin",
    editor = "Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W18-5605/",
    doi = "10.18653/v1/W18-5605",
    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."
}