@InProceedings{xherija:2018:SMM4H,
  author    = {Xherija, Orest},
  title     = {Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention},
  booktitle = {Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-5910}
}