@inproceedings{coltekin-rama-2018-drug,
    title = "Drug-Use Identification from Tweets with Word and Character N-Grams",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka},
    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/jlcl-multiple-ingestion/W18-5914/",
    doi = "10.18653/v1/W18-5914",
    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 91.22, 46.8, 42.4, and 85.53 on tasks 1, 2, 3, and 4 respectively."
}