@inproceedings{ngo-etal-2019-neural,
    title = "Neural Dependency Parsing of Biomedical Text: {T}urku{NLP} entry in the {CRAFT} Structural Annotation Task",
    author = "Ngo, Thang Minh  and
      Kanerva, Jenna  and
      Ginter, Filip  and
      Pyysalo, Sampo",
    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/jlcl-multiple-ingestion/D19-5728/",
    doi = "10.18653/v1/D19-5728",
    pages = "206--215",
    abstract = "We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing. Our approach builds primarily on the Turku neural parser, a native dependency parser that ranked among the best in the recent CoNLL tasks on parsing Universal Dependencies. To adapt the parser to the biomedical domain, we considered and evaluated a number of approaches, including the generation of custom word embeddings, combination with other in-domain resources, and the incorporation of information from named entity recognition. We achieved a labeled attachment score of 89.7{\%}, the best result among task participants."
}