@inproceedings{aroyehun-gelbukh-2019-detection,
    title = "Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings",
    author = "Aroyehun, Segun Taofeek  and
      Gelbukh, Alexander",
    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/fix-sig-urls/W19-3224/",
    doi = "10.18653/v1/W19-3224",
    pages = "133--135",
    abstract = "This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209."
}