Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task

Thang Minh Ngo, Jenna Kanerva, Filip Ginter, Sampo Pyysalo


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.
Anthology ID:
D19-5728
Volume:
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
BioNLP | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
206–215
Language:
URL:
https://www.aclweb.org/anthology/D19-5728
DOI:
10.18653/v1/D19-5728
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/D19-5728.pdf