@inproceedings{mao-liu-2019-integration,
    title = "Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature",
    author = "Mao, Jihang  and
      Liu, Wanli",
    editor = "Jin-Dong, Kim  and
      Claire, N{\'e}dellec  and
      Robert, Bossy  and
      Louise, Del{\'e}ger",
    booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-5724/",
    doi = "10.18653/v1/D19-5724",
    pages = "168--173",
    abstract = "In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks."
}