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        <title>Deep Learning for Identification of Adverse Effect Mentions In Twitter Data</title>
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        <namePart type="given">Paul</namePart>
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        <namePart type="given">Ozlem</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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        <name type="personal">
            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
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    <abstract>Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.</abstract>
    <identifier type="citekey">barry-uzuner-2019-deep</identifier>
    <identifier type="doi">10.18653/v1/W19-3215</identifier>
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%0 Conference Proceedings
%T Deep Learning for Identification of Adverse Effect Mentions In Twitter Data
%A Barry, Paul
%A Uzuner, Ozlem
%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 barry-uzuner-2019-deep
%X Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.
%R 10.18653/v1/W19-3215
%U https://aclanthology.org/W19-3215
%U https://doi.org/10.18653/v1/W19-3215
%P 99-101

Markdown (Informal)

[Deep Learning for Identification of Adverse Effect Mentions In Twitter Data](https://aclanthology.org/W19-3215) (Barry & Uzuner, ACL 2019)

• Deep Learning for Identification of Adverse Effect Mentions In Twitter Data (Barry & Uzuner, ACL 2019)

ACL

• Paul Barry and Ozlem Uzuner. 2019. Deep Learning for Identification of Adverse Effect Mentions In Twitter Data. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 99–101, Florence, Italy. Association for Computational Linguistics.