CoRTEx:通过解释与应用构建生物医学知识图的应用来表示术语的对比学习
Huaiyuan Ying1, Zhengyun Zhao1, Yang Zhao2
1Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China.
概括
通过解释表示术语的对比学习 (CoRTEx) 使用大型语言模型来改进生物医学术语集群. 这种方法提高了构建生物医学知识图的准确性和通用性.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 生物医学知识图对研究至关重要.
- 术语聚类,识别同义词,是构建这些图表的关键.
- 现有的对比式学习模型与困难的术语扎,并且缺乏超出统一医疗语言系统 (UMLS) 的概括性.
研究的目的:
- 增强术语表示和改善生物医学知识图的术语聚类.
- 利用大型语言模型 (LLM) 的世界知识来克服以前模型的局限性.
- 引入一种新的方法,即通过解释表示术语的对比学习 (CoRTEx).
主要方法:
- 使用ChatGPT生成对UMLS术语的解释.
- 采用对比式学习,同时嵌入术语和解释.
- 引入了硬负样和一个ChatGPT辅助的BIRCH算法,以实现高效的集群.
主要成果:
- 在已建立的集群和硬负测试集中,CoRTEx获得了最高的F1分数.
- 从生物医学信息学本体系统 (BIOS) 聚合了超过3500万个术语,成为超过2200万个集群.
- 通过案例研究证明了在处理具有挑战性的样品方面的有效性.
结论:
- 与基准模型相比,CoRTEx显示出更高的准确性和稳定性.
- 将术语与它们的解释对齐,可以提高聚类的性能.
- 该方法适用于大规模的生物医学本体学术语集群.
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