通过基于LLM的文本简化来改善复杂实体提及的生物医学实体链接
Florian Borchert1, Ignacio Llorca1, Matthieu-P Schapranow1
1Hasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam 14482, Germany.
概括
使用生成性大语言模型简化复杂的医学术语可以提高生物医学文本中实体链接的准确性. 这种方法提高了回忆和top-1准确性,用于识别医疗概念.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
背景情况:
- 生物医学研究和医疗保健产生大量的自由文本数据.
- 实体链接对于通过自然语言处理 (NLP) 访问这些信息至关重要.
- 复杂的,多令牌实体提及对准确的规范化和概念映射提出了挑战.
研究的目的:
- 开发一种用于预处理生物医学文本中复杂实体提及的方法.
- 用文本简化改进实体链接的候选生成.
- 评估BioCreative VIII SympTEMIST共享任务的方法.
主要方法:
- 使用生成性大语言模型来简化复杂实体提及的文本.
- 使用生成预训练式变压器 (GPT) 模型应用几次射击提示.
- 将简化提及集成到一个实体链接管道中,用于候选人生成和重新排名.
主要成果:
- 文本简化导致更容易规范化的提及范围.
- 在候选人世代期间的回忆提高了2.9个百分点.
- 通过将回忆改进转化为重新排名,提高了top-1准确度,在测试组中达到63.6%.
结论:
- 生成型的大型语言模型可以有效地简化复杂的生物医学实体,以改善NLP.
- 拟议的文本简化方法提高了实体链接的性能,特别是回忆和准确性.
- 该方法已集成到开源的xMEN工具包中,以实现更广泛的应用.
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