@inproceedings{gondane-2019-neural,
    title = "Neural Network to Identify Personal Health Experience Mention in Tweets Using {B}io{BERT} Embeddings",
    author = "Gondane, Shubham",
    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/W19-3218/",
    doi = "10.18653/v1/W19-3218",
    pages = "110--113",
    abstract = "This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively."
}