@inproceedings{nguyen-verspoor-2018-convolutional,
    title = "Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings",
    author = "Nguyen, Dat Quoc  and
      Verspoor, Karin",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-2314/",
    doi = "10.18653/v1/W18-2314",
    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."
}