SE-MSLC:语义透驱动的关键词分析和多阶段的逻辑组合回忆用于搜索引擎
Haihua Lu1, Liang Yu1,2,3, Yantao He1
1School of Computer Science, Guangdong University of Science and Technology, Dongguan 523083, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
本研究引入了一种新的信息检索框架 (SE-MSLC),使用语义来提高搜索引擎的准确性. SE-MSLC框架增强了关键词分析和回忆策略,以更好地识别用户意图和搜索结果.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
背景情况:
- 信息检索对于访问数据至关重要.
- 现有的方法在垂直域搜索引擎中难以准确.
研究的目的:
- 提出一个信息检索框架 (SE-MSLC),通过信息理论提高检索效率.
- 提高智能垂直域搜索引擎中搜索结果的质量.
主要方法:
- 开发了一种语义驱动的关键词重要性分析 (SE-KIA) 以基于日志,语料库和语义的动态权重查询关键词.
- 实施混合召回策略 (HRS-MSLC),结合多阶段和逻辑组合策略,用于多细分词段和"AND"/"OR"逻辑.
- 通过优先考虑具有高信息内容的关键字来管理检索不确定性.
主要成果:
- 在SE-MSLC框架下,成功率@1提高了7.3%,成功率@3提高了6.6%.
- 在垂直域搜索引擎中有效地解决和解决有问题的案例.
- 在检索结果数量和相关性之间取得了卓越的平衡.
结论:
- 拟议的SE-MSLC框架显著提高了垂直领域的信息检索效率.
- 语义和高级回忆策略是提高搜索引擎性能和用户满意度的关键.
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