@inproceedings{ge-etal-2019-detecting,
    title = "Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention",
    author = "Ge, Suyu  and
      Qi, Tao  and
      Wu, Chuhan  and
      Huang, Yongfeng",
    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/ingest-emnlp/W19-3214/",
    doi = "10.18653/v1/W19-3214",
    pages = "96--98",
    abstract = "This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2."
}