Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task
: Thang Minh Ngo, Jenna Kanerva, Filip Ginter, Sampo Pyysalo
: Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
: Workshop on BioNLP Open Shared Tasks
: 2019
: Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
: 206
: 215
: 978-1-950737-82-6
DOI: https://doi.org/10.18653/v1/D19-5728
: https://www.aclweb.org/anthology/D19-5728/
: https://research.utu.fi/converis/portal/detail/Publication/45036030
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.
