基于罗伯特模型的术语领域的短文本相似性计算的研究
Fei Chen1, Zhenling Zhang1, Yangli Jia2
1School of Computer Science, and the Knowledge Engineering & Terminology Research Centre (KETRC), Liaocheng University, Liaocheng, 252000, Shandong, China.
Scientific reports
|October 22, 2025
概括
本研究引入了一种计算术语域短文相似性的新方法,克服数据稀缺性并改善语义理解. SDQKC模型在基准数据集上实现了卓越的性能.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 信息检索 信息检索
背景情况:
- 术语域短文本相似性计算面临的挑战是有限的数据集和浅层语义分析.
- 现有的方法很难在专业领域捕捉微妙的语义关系.
研究的目的:
- 为了解决数据集稀缺性,并加强深度语义提取,以获得术语域短文相似性.
- 开发和验证一种新型模型,以提高相似性计算的准确性.
主要方法:
- 使用GPT-4和专家验证的自动化管道构建了多域术语定义相似性 (TDS) 数据集.
- 开发了SDQKC (斯伯特+动态QK+对比) 模型,优化语BERT,使用动态QK共同注意力和对比学习.
主要成果:
- 在SDQKC模型中,BQ集团的皮尔森相关系数为0.69384,TDS数据集的皮尔森相关系数为0.69511.
- 与基线模型相比,表现出显著的性能改进,验证了拟议方法的有效性.
结论:
- 拟议的SDQKC模型有效地增强了深度语义理解,并在术语域短文相似性方面优于现有的方法.
- 自动化TDS数据集构建管道为未来该领域的研究提供了宝贵的资源.
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