@inproceedings{cortes-tejada-etal-2019-nlp,
    title = "{NLP}@{UNED} at {SMM}4{H} 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets",
    author = "Cortes-Tejada, Javier  and
      Martinez-Romo, Juan  and
      Araujo, Lourdes",
    editor = "Weissenbacher, Davy  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-3213/",
    doi = "10.18653/v1/W19-3213",
    pages = "93--95",
    abstract = "This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings."
}