Data Augmentation Techniques for Chinese Disease Name Normalization
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909447096369152 |
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| author | Cui, Wenqian Fu, Xiangling Liu, Shaohui Gu, Mingjun Liu, Xien Wu, Ji King, Irwin |
| author_facet | Cui, Wenqian Fu, Xiangling Liu, Shaohui Gu, Mingjun Liu, Xien Wu, Ji King, Irwin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Augmentation Techniques for Chinese Disease Name Normalization Cui, Wenqian Fu, Xiangling Liu, Shaohui Gu, Mingjun Liu, Xien Wu, Ji King, Irwin Computation and Language Artificial Intelligence 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 |
| title | Data Augmentation Techniques for Chinese Disease Name Normalization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01195 |