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        <title>Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media</title>
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        <namePart type="given">Pilar</namePart>
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        <namePart type="given">Manuel</namePart>
        <namePart type="given">Carlos</namePart>
        <namePart type="family">Díaz Galiano</namePart>
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    <name type="personal">
        <namePart type="given">Maite</namePart>
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    <name type="personal">
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        <namePart type="given">Alfonso</namePart>
        <namePart type="family">Urena Lopez</namePart>
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            <title>Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop &amp; Shared Task</title>
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            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
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        <name type="personal">
            <namePart type="given">Graciela</namePart>
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    <abstract>Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.</abstract>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
%A López Úbeda, Pilar
%A Díaz Galiano, Manuel Carlos
%A Martin, Maite
%A Urena Lopez, L. Alfonso
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F lopez-ubeda-etal-2019-using-machine
%X Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.
%R 10.18653/v1/W19-3216
%U https://aclanthology.org/W19-3216
%U https://doi.org/10.18653/v1/W19-3216
%P 102-106

Markdown (Informal)

[Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media](https://aclanthology.org/W19-3216) (López Úbeda et al., ACL 2019)

• Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media (López Úbeda et al., ACL 2019)

ACL

• Pilar López Úbeda, Manuel Carlos Díaz Galiano, Maite Martin, and L. Alfonso Urena Lopez. 2019. Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 102–106, Florence, Italy. Association for Computational Linguistics.