Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection
Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection
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DOI：
10.18653/v1/2022.bionlp-1.29
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Bosung Kim;Ndapandula Nakashole
中科院分区：
文献类型：
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作者：
Bosung Kim;Ndapandula Nakashole
我们研究的问题，实体检测和规范化应用于病人的症状，出现疫苗的副作用的自我报告。我们的应用领域提出了独特的挑战，使传统的分类方法无效：实体类型的数量很大;许多症状是罕见的，导致每个实体类型的训练样本的长尾分布。我们用一个自回归模型来应对这些挑战，这个模型可以生成症状的标准化名称。我们引入了一种数据增强技术，以增加罕见症状的训练示例数量。对真实患者疫苗症状自我报告的实验表明，我们的方法优于强基线，并且额外的示例提高了长尾实体的性能。
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.