Using Snomed to recognize and index chemical and drug mentions.

Pilar López Úbeda, Manuel Carlos Díaz Galiano, L. Alfonso Urena Lopez, Maite Martin

Abstract
In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78% in F1 score on the first sub-track and in the second task we map with Snomed correctly 72% of the found entities.
Anthology ID:
D19-5718
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:
115–120
Language:
URL:
https://aclanthology.org/D19-5718
DOI:
10.18653/v1/D19-5718
Bibkey:
Cite (ACL):
Pilar López Úbeda, Manuel Carlos Díaz Galiano, L. Alfonso Urena Lopez, and Maite Martin. 2019. Using Snomed to recognize and index chemical and drug mentions.. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 115–120, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Using Snomed to recognize and index chemical and drug mentions. (López Úbeda et al., BioNLP 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/D19-5718.pdf
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