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    <titleInfo>
        <title>Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo</title>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="family">Sarabadani</namePart>
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        <dateIssued>2019-08</dateIssued>
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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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.</abstract>
    <identifier type="citekey">sarabadani-2019-detection</identifier>
    <identifier type="doi">10.18653/v1/W19-3221</identifier>
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        <url>https://aclanthology.org/W19-3221</url>
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        <date>2019-08</date>
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            <start>120</start>
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%0 Conference Proceedings
%T Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo
%A Sarabadani, Sarah
%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 sarabadani-2019-detection
%X This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
%R 10.18653/v1/W19-3221
%U https://aclanthology.org/W19-3221
%U https://doi.org/10.18653/v1/W19-3221
%P 120-122

Markdown (Informal)

[Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo](https://aclanthology.org/W19-3221) (Sarabadani, ACL 2019)

• Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo (Sarabadani, ACL 2019)

ACL

• Sarah Sarabadani. 2019. Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 120–122, Florence, Italy. Association for Computational Linguistics.