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        <title>Using Snomed to recognize and index chemical and drug mentions.</title>
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        <namePart type="given">Pilar</namePart>
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    <name type="personal">
        <namePart type="given">Manuel</namePart>
        <namePart type="given">Carlos</namePart>
        <namePart type="family">Díaz Galiano</namePart>
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        <namePart type="given">Alfonso</namePart>
        <namePart type="family">Urena Lopez</namePart>
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    <name type="personal">
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            <namePart type="given">Kim</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Using Snomed to recognize and index chemical and drug mentions.
%A López Úbeda, Pilar
%A Díaz Galiano, Manuel Carlos
%A Urena Lopez, L. Alfonso
%A Martin, Maite
%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 lopez-ubeda-etal-2019-using
%X 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.
%R 10.18653/v1/D19-5718
%U https://aclanthology.org/D19-5718
%U https://doi.org/10.18653/v1/D19-5718
%P 115-120

Markdown (Informal)

[Using Snomed to recognize and index chemical and drug mentions.](https://aclanthology.org/D19-5718) (López Úbeda et al., BioNLP 2019)

• Using Snomed to recognize and index chemical and drug mentions. (López Úbeda et al., BioNLP 2019)

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.