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        <title>Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination</title>
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    <abstract>This paper reports on the performance of Edinburgh_UCL_Health’s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for self-report).</abstract>
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%0 Conference Proceedings
%T Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
%A Guellil, Imane
%A Wu, Jinge
%A Wu, Honghan
%A Sun, Tony
%A Alex, Beatrice
%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 guellil-etal-2022-edinburgh
%X This paper reports on the performance of Edinburgh_UCL_Health’s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for self-report).
%U https://aclanthology.org/2022.smm4h-1.40
%P 148-152

Markdown (Informal)

[Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination](https://aclanthology.org/2022.smm4h-1.40) (Guellil et al., SMM4H 2022)

• Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination (Guellil et al., SMM4H 2022)

ACL

• Imane Guellil, Jinge Wu, Honghan Wu, Tony Sun, and Beatrice Alex. 2022. Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 148–152, Gyeongju, Republic of Korea. Association for Computational Linguistics.