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为了提高专利检索的效率质量以及专利审查质量，构建语义改写模型。介绍了基于模型的语义改写技巧，重点从技术主题应用领域的扩展、关键部件作用/功能的扩展和关键技术手段/技术特征权重值的增加这3个方向分析语义改写机理和改写方法，并结合深度学习的语义分析能力优化模型的语义改写能力。所提语义改写模型提高了大模型检索的命中率、召回率和精确率，可助力大模型在专利审查工作中的应用，为专利质量的提升和专利事业的可持续发展提供有力支持。
Abstract：Semantic rewriting models were established to improve the efficiency and quality of patent retrieval, as well as the patent review quality, and model-based semantic rewriting skills were introduced. Mechanisms and methods of rewriting for semantic rewriting were analyzed in three directions with focus on expansion of technical subject application areas, expansion of key component roles/functions and increase of key technical means/technical feature weight values. Moreover, semantic rewriting capabilities of the model was optimized via semantic analysis capabilities combined with deep learning. The proposed rewriting model improves hit rate, recall rate and accuracy of large model retrieval, which facilitates the use of large models in patent review, and can provide strong support for improvements in patent quality and sustainable development of patent cause.
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基本信息:
中图分类号:G255.53;TP391.3;TP18
引用信息:
[1]冷林霞.大模型检索之通用类结构检索的语义改写技巧[J].中国科技论文,2025,20(10):856-863.