@InProceedings{joshi-EtAl:2018:SMM4H,
  author    = {Joshi, Aditya  and  Dai, Xiang  and  Karimi, Sarvnaz  and  Sparks, Ross  and  Paris, Cecile  and  MacIntyre, C Raina},
  title     = {Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection},
  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     = {43--47},
  abstract  = {Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.},
  url       = {http://www.aclweb.org/anthology/W18-5911}
}