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        <title>Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection</title>
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            <title>Proceedings of the 21st Workshop on Biomedical Language Processing</title>
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    <abstract>We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines. Our application domain presents unique challenges that render traditional classification methods ineffective: the number of entity types is large; and many symptoms are rare, resulting in a long-tail distribution of training examples per entity type. We tackle these challenges with an autoregressive model that generates standardized names of symptoms. We introduce a data augmentation technique to increase the number of training examples for rare symptoms. Experiments on real-life patient vaccine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.</abstract>
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%0 Conference Proceedings
%T Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection
%A Kim, Bosung
%A Nakashole, Ndapa
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 21st Workshop on Biomedical Language Processing
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kim-nakashole-2022-data
%X We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines. Our application domain presents unique challenges that render traditional classification methods ineffective: the number of entity types is large; and many symptoms are rare, resulting in a long-tail distribution of training examples per entity type. We tackle these challenges with an autoregressive model that generates standardized names of symptoms. We introduce a data augmentation technique to increase the number of training examples for rare symptoms. Experiments on real-life patient vaccine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.
%R 10.18653/v1/2022.bionlp-1.29
%U https://aclanthology.org/2022.bionlp-1.29
%U https://doi.org/10.18653/v1/2022.bionlp-1.29
%P 310-315

Markdown (Informal)

[Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection](https://aclanthology.org/2022.bionlp-1.29) (Kim & Nakashole, BioNLP 2022)

• Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection (Kim & Nakashole, BioNLP 2022)

ACL

• Bosung Kim and Ndapa Nakashole. 2022. Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 310–315, Dublin, Ireland. Association for Computational Linguistics.