@inproceedings{sarabadani-2019-detection,
    title = "Detection of Adverse Drug Reaction Mentions in Tweets Using {ELM}o",
    author = "Sarabadani, Sarah",
    editor = "Weissenbacher, Davy  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-3221/",
    doi = "10.18653/v1/W19-3221",
    pages = "120--122",
    abstract = "This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets."
}