@inproceedings{rivera-martinez-2019-deep,
    title = "Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in {S}panish clinical text",
    author = "Rivera, Renzo  and
      Mart{\'i}nez, Paloma",
    editor = "Jin-Dong, Kim  and
      Claire, N{\'e}dellec  and
      Robert, Bossy  and
      Louise, Del{\'e}ger",
    booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-5707/",
    doi = "10.18653/v1/D19-5707",
    pages = "38--46",
    abstract = "In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features. The approach was evaluated on the PharmaCoNER Corpus obtaining an F-measure of 85.24{\%} for subtask 1 and 49.36{\%} for subtask2. These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-the-art approaches."
}