Data Augmentation Techniques for Chinese Disease Name Normalization

TL;DR

This paper introduces a novel data augmentation approach for Chinese disease name normalization, significantly improving performance in low-data scenarios by addressing training data scarcity.

Contribution

The paper proposes a new data augmentation method specifically designed for Chinese disease normalization, enhancing model performance with limited training data.

Findings

Significant performance improvements across baseline models

Effective in scenarios with limited training data

Versatile augmentation techniques for disease normalization

Abstract

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data