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        <title>NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets</title>
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    <name type="personal">
        <namePart type="given">Javier</namePart>
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        <namePart type="given">Juan</namePart>
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    <name type="personal">
        <namePart type="given">Lourdes</namePart>
        <namePart type="family">Araujo</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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        <name type="personal">
            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
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    <abstract>This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3213</identifier>
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%0 Conference Proceedings
%T NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets
%A Cortes-Tejada, Javier
%A Martinez-Romo, Juan
%A Araujo, Lourdes
%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 cortes-tejada-etal-2019-nlp
%X This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.
%R 10.18653/v1/W19-3213
%U https://aclanthology.org/W19-3213
%U https://doi.org/10.18653/v1/W19-3213
%P 93-95

Markdown (Informal)

[NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets](https://aclanthology.org/W19-3213) (Cortes-Tejada et al., ACL 2019)

• NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets (Cortes-Tejada et al., ACL 2019)

ACL

• Javier Cortes-Tejada, Juan Martinez-Romo, and Lourdes Araujo. 2019. NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 93–95, Florence, Italy. Association for Computational Linguistics.