@inproceedings{rao-etal-2017-biomedical,
    title = "Biomedical Event Extraction using {A}bstract {M}eaning {R}epresentation",
    author = "Rao, Sudha  and
      Marcu, Daniel  and
      Knight, Kevin  and
      Daum{\'e} III, Hal",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-2315/",
    doi = "10.18653/v1/W17-2315",
    pages = "126--135",
    abstract = "We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results."
}