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

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

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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
Editors:
Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
206–215
Language:
URL:
https://aclanthology.org/D19-5728
DOI:
10.18653/v1/D19-5728
Bibkey:
Cite (ACL):
Thang Minh Ngo, Jenna Kanerva, Filip Ginter, and Sampo Pyysalo. 2019. Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 206–215, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task (Ngo et al., BioNLP 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D19-5728.pdf
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