@inproceedings{saha-etal-2018-leveraging,
    title = "Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets",
    author = "Saha, Rupsa  and
      Naskar, Abir  and
      Dasgupta, Tirthankar  and
      Dey, Lipika",
    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/moar-dois/W18-5919/",
    doi = "10.18653/v1/W18-5919",
    pages = "67--69",
    abstract = "In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models with the dataset as released by the SMM4H workshop shared Task-1 and Task-3 respectively. Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78.5{\%}, 88{\%} and 82.9{\%} respectively for Task1 and 33.2{\%}, 54.7{\%} and 41.3{\%} for Task3."
}