@inproceedings{bagherzadeh-bergler-2021-leveraging,
    title = "Leveraging knowledge sources for detecting self-reports of particular health issues on social media",
    author = "Bagherzadeh, Parsa  and
      Bergler, Sabine",
    editor = "Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis",
    month = apr,
    year = "2021",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.louhi-1.5/",
    pages = "38--48",
    abstract = "This paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories. We claim that resources such as the MeSH hierarchy and currently available parse information are effective extensions of moderately sized training datasets for various fine-grained tweet classification tasks of self-reported health issues."
}