Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task
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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D19-5728.pdf