@inproceedings{goodrum-etal-2019-extraction,
    title = "Extraction of Lactation Frames from Drug Labels and {L}act{M}ed",
    author = "Goodrum, Heath  and
      Gudala, Meghana  and
      Misra, Ankita  and
      Roberts, Kirk",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-5020/",
    doi = "10.18653/v1/W19-5020",
    pages = "191--200",
    abstract = "This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed). A frame semantic approach is utilized, and the paper describes the selected frames, their annotation on a set of 900 sections from drug labels and LactMed articles, and the NLP system to extract such frame instances automatically. The ultimate goal of the project is to use such a system to identify discrepancies in lactation-related drug information between these resources."
}