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SemNovel - 一种新的方法来检测生物医学出版物的语义新性,使用嵌入大型语言模型

Xueqing Peng¹, Yutong Xie², Huan He¹

¹Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, 100 College St, New Haven, CT 06510, USA.

Journal of biomedical informatics

|November 16, 2025

概括

SemNovel使用大型语言模型 (LLM) 来量化生物医学研究中的语义新性. 这种方法与未来的研究影响相关,并确定了突破性的研究.

科学领域:

生物医学研究的研究.
科学文献分析,科学文献分析.
计算语言学计算语言学

背景情况:

越来越多的科学文献需要新的贡献识别.
当前的新性措施缺乏语义上下文.
现有的方法使用孤立的特征,缺少细微的文本含义.

研究的目的:

介绍SemNovel,一个语义新检测框架.
利用大型语言模型 (LLM) 来获取更丰富的语义内容.
开发一种可扩展和强大的方法来量化生物医学文献中的新奇性.

主要方法:

使用LLM嵌入器 (BAAI/llm嵌入器) 来构建语义宇宙.
使用t分布式静态邻居嵌入 (t-SNE) 来实现PubMed库的二维可视化.
根据文章与以前出版物之间的距离计算SemNovel评分.

主要成果:

SemNovel评分与未来研究影响 (引用数) 有积极的相关性.
优于以前的语义新指标,并识别了诺贝尔奖获得者研究.
揭示了研究趋势,并强调了跨学科合作在创新中的作用.

结论:

SemNovel提供了一个可扩展和强大的方法来量化语义新性.
提供了一个揭示突破性的研究和跟踪科学进展的工具.
方便分析生物医学文献中的创新趋势.

关键词:

大型语言模型.文学采矿的文学采矿.新奇发现检测新奇发现检测语义空间是一个语义空间.

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