开发基于SAO结构的自动标记主题建模技术分析
Minyoung Park1, Sunhye Kim2, Byungun Yoon3
1Master student, Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, Seoul, Korea.
PloS one
|August 26, 2025
概括
这项研究引入了一种新的主体-动作-对象 (SAO) 方法,用于专利中的主题建模,改进技术趋势分析. 该方法提高了自动标记的准确性,并为技术管理提供了更深入的语义洞察力.
科学领域:
- 信息科学
- 技术管理
- 计算语言学
背景情况:
- 主题建模对于识别技术趋势和预测进步至关重要.
- 专利文件中现有的自动标签方法在量化评估和捕捉技术层次上存在局限性.
- 传统的方法往往忽视了技术的功能意义.
研究的目的:
- 在专利文件中引入基于主体行动对象 (SAO) 的主题建模方法.
- 通过使用"SAO袋"概念克服现有的自动标签方法的局限性.
- 提高技术趋势分析的准确性和可解释性.
主要方法:
- 开发了一种自动标记方法,结合了SAO主题建模和评分.
- 综合文本总结和网络分析以进行全面评估.
- 纳入基于合作专利分类 (CPC) 的分类层次结构以提高可解释性.
主要成果:
- 拟议的模型在技术背景下捕捉功能意义方面表现出有效性,使用ROUGE,相关性,覆盖范围和歧视得分进行评估.
- 基于SAO的方法提高了主题标签的准确性,并提供了更深入的语义洞察力.
- 层次结构增强了对技术发展和趋势的看法.
结论:
- 基于SAO的自动标签方法为专利文件中的主题建模提供了更准确和更易于解释的方法.
- 这种方法有助于更有效的技术管理,创新和政策制定.
- 这项研究为技术预测和诊断的精细方法奠定了基础.
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