@inproceedings{khademi-etal-2020-adverse,
    title = "Adverse Drug Reaction Detection in {T}witter Using {R}o{BERT}a and Rules",
    author = "Khademi, Sedigh  and
      Delirhaghighi, Pari  and
      Burstein, Frada",
    editor = "Gonzalez-Hernandez, Graciela  and
      Klein, Ari Z.  and
      Flores, Ivan  and
      Weissenbacher, Davy  and
      Magge, Arjun  and
      O'Connor, Karen  and
      Sarker, Abeed  and
      Minard, Anne-Lyse  and
      Tutubalina, Elena  and
      Miftahutdinov, Zulfat  and
      Alimova, Ilseyar",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.smm4h-1.18/",
    pages = "113--117",
    abstract = "This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model{'}s F1-Score of 0.56 on the test dataset was 10{\%} better than the mean of the F1-Score of the best submissions in the task."
}