@inproceedings{xherija-2018-classification,
    title = "Classification of Medication-Related Tweets Using Stacked Bidirectional {LSTM}s with Context-Aware Attention",
    author = "Xherija, Orest",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy  and
      Sarker, Abeed  and
      Paul, Michael",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W18-5910/",
    doi = "10.18653/v1/W18-5910",
    pages = "38--42",
    abstract = "This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available."
}