@InProceedings{ltekin-rama:2018:SMM4H,
  author    = {Çöltekin, Çağrı  and  Rama, Taraka},
  title     = {Drug-Use Identification from Tweets with Word and Character N-Grams},
  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     = {52--53},
  abstract  = {This paper describes our systems in social media mining for health applications (SMM4H) shared task. We participated in all four tracks of the shared task using linear models with a combination of character and word n-gram features. We did not use any external data or domain specific information. The resulting systems achieved above-average scores among other participating systems, with F1-scores of},
  url       = {http://www.aclweb.org/anthology/W18-5914}
}