An ensemble CNN method for biomedical entity normalization

Pan Deng, Haipeng Chen, Mengyao Huang, Xiaowen Ruan, Liang Xu


Abstract
Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.
Anthology ID:
D19-5721
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:
143–149
Language:
URL:
https://aclanthology.org/D19-5721
DOI:
10.18653/v1/D19-5721
Bibkey:
Cite (ACL):
Pan Deng, Haipeng Chen, Mengyao Huang, Xiaowen Ruan, and Liang Xu. 2019. An ensemble CNN method for biomedical entity normalization. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 143–149, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
An ensemble CNN method for biomedical entity normalization (Deng et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5721.pdf
Code
 OXPHOS/BioNLP
Data
BBBB-norm-habitatBB-norm-phenotype