@inproceedings{deng-etal-2019-ensemble,
title = "An ensemble {CNN} method for biomedical entity normalization",
author = "Deng, Pan and
Chen, Haipeng and
Huang, Mengyao and
Ruan, Xiaowen and
Xu, Liang",
editor = "Jin-Dong, Kim and
Claire, N{\'e}dellec and
Robert, Bossy and
Louise, Del{\'e}ger",
booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5721",
doi = "10.18653/v1/D19-5721",
pages = "143--149",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T An ensemble CNN method for biomedical entity normalization
%A Deng, Pan
%A Chen, Haipeng
%A Huang, Mengyao
%A Ruan, Xiaowen
%A Xu, Liang
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F deng-etal-2019-ensemble
%X 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.
%R 10.18653/v1/D19-5721
%U https://aclanthology.org/D19-5721
%U https://doi.org/10.18653/v1/D19-5721
%P 143-149
Markdown (Informal)
[An ensemble CNN method for biomedical entity normalization](https://aclanthology.org/D19-5721) (Deng et al., BioNLP 2019)
ACL