一个多维的语义伪相关性反框架用于信息检索
Min Pan1, Yu Liu2, Jinguang Chen3
1College of Computer and Information Engineering, Hubei Normal University, Huangshi, China.
Scientific reports
|December 31, 2024
概括
这项研究通过使用预训练模型来提取伪相关文档中的语义信息来增强查询扩展来增强信息检索. 这种方法可以提高文档排名和检索性能.
科学领域:
- 信息检索 信息检索
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 预先训练好的模型是提高信息检索中的文档排名的关键.
- 当前的方法往往限制从预训练模型的语义信息记录重新排名,忽视查询扩展.
- 伪相关文档包含有效查询扩展的关键语义信息.
研究的目的:
- 提出一种新的方法,利用预训练模型从伪相关文档中提取多维语义信息.
- 通过利用更丰富的语义见解来增强查询扩展.
- 为了提高整体信息检索性能.
主要方法:
- 为了提高效率,使用传统的稀疏方法 (例如BM25) 进行初始检索.
- 预训练的模型编码查询和记录段落,以提取句子级和段落级的语义相似性.
- 将术语级别权重与提取的语义权重结合起来,以生成第二个检索轮的改进查询.
主要成果:
- 在五个TREC数据集和一个医疗数据集上的实验表明了显著的改善.
- 关键指标,如平均精度 (MAP) 和精度在10 (P@10) 显示了积极的收益.
- 这种方法有效地利用了多维语义信息来扩展查询.
结论:
- 从伪相关文档中利用多维语义信息显著优化查询扩展.
- 拟议的方法提供了一种更有效的方式来利用语义信息,以提高检索性能.
- 这项研究为通过先进的语义分析改进信息检索系统提供了宝贵的见解.
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