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The disease is a core concept in the medical field, and the task ofnormalizing disease names is the basis of all disease-related tasks. However,due to the multi-axis and multi-grain nature of disease names, incorrectinformation is often injected and harms the performance when using general textdata augmentation techniques. To address the above problem, we propose a set ofdata augmentation techniques that work together as an augmented training taskfor disease normalization. Our data augmentation methods are based on both theclinical disease corpus and standard disease corpus derived from ICD-10 coding.Extensive experiments are conducted to show the effectiveness of our proposedmethods. The results demonstrate that our methods can have up to 3\%performance gain compared to non-augmented counterparts, and they can work evenbetter on smaller datasets.

Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization