@inproceedings{kalyan-sangeetha-2020-want,
    title = "Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use {R}o{BERT}a",
    author = "Kalyan, Katikapalli Subramanyam  and
      Sangeetha, Sivanesan",
    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-1.20/",
    pages = "121--124",
    abstract = "This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task 3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58{\%} in task 2 which is 12{\%} more than the average score b) relaxed F1-score of 70.1{\%} in ADR extraction of task 3 which is 13.7{\%} more than the average score and relaxed F1-score of 35{\%} in ADR extraction + normalization of task 3 which is 5.8{\%} more than the average score. Overall, our models achieve promising results in both the tasks with significant improvements over average scores."
}