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        <title>BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models</title>
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        <namePart type="given">Edgar</namePart>
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        <namePart type="given">José</namePart>
        <namePart type="given">Luis</namePart>
        <namePart type="family">Oliveira</namePart>
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        <namePart type="given">Alina</namePart>
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    <name type="personal">
        <namePart type="given">Olga</namePart>
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            <title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop &amp; Shared Task</title>
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            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
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            <namePart type="given">Davy</namePart>
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    <abstract>This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.</abstract>
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%0 Conference Proceedings
%T BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models
%A Morais, Edgar
%A Oliveira, José Luis
%A Trifan, Alina
%A Fajarda, Olga
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F morais-etal-2022-bioinfo
%X This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.
%U https://aclanthology.org/2022.smm4h-1.19
%P 65-67

Markdown (Informal)

[BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models](https://aclanthology.org/2022.smm4h-1.19) (Morais et al., SMM4H 2022)

• BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models (Morais et al., SMM4H 2022)

ACL

• Edgar Morais, José Luis Oliveira, Alina Trifan, and Olga Fajarda. 2022. BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 65–67, Gyeongju, Republic of Korea. Association for Computational Linguistics.