@inproceedings{lange-etal-2019-nlnde,
    title = "{NLNDE}: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection",
    author = {Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    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/D19-5705/",
    doi = "10.18653/v1/D19-5705",
    pages = "26--32",
    abstract = "Named entity recognition has been extensively studied on English news texts. However, the transfer to other domains and languages is still a challenging problem. In this paper, we describe the system with which we participated in the first subtrack of the PharmaCoNER competition of the BioNLP Open Shared Tasks 2019. Aiming at pharmacological entity detection in Spanish texts, the task provides a non-standard domain and language setting. However, we propose an architecture that requires neither language nor domain expertise. We treat the task as a sequence labeling task and experiment with attention-based embedding selection and the training on automatically annotated data to further improve our system{'}s performance. Our system achieves promising results, especially by combining the different techniques, and reaches up to 88.6{\%} F1 in the competition."
}