@inproceedings{minard-etal-2018-irisa,
    title = "{IRISA} at {SMM}4{H} 2018: Neural Network and Bagging for Tweet Classification",
    author = "Minard, Anne-Lyse  and
      Raymond, Christian  and
      Claveau, Vincent",
    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/fix-sig-urls/W18-5913/",
    doi = "10.18653/v1/W18-5913",
    pages = "50--51",
    abstract = "This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4."
}