一种选择性的干方法,以尽量减少信息检索系统故障的风险
Gökhan Göksel1, Ahmet Arslan1, Bekir Taner Dinçer2
1Computer Engineering, Eskişehir Technical University, Eskisehir, Turkey.
PeerJ. Computer science
|June 22, 2023
概括
选择性源改进信息检索 (IR) 通过决定何时应用于每个查询的源. 这种方法最大限度地减少了性能损失,并提高了文档检索准确度.
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:
- 干系是一种常见的技术,用于提高信息检索 (IR) 系统的性能.
- 然而,实验结果表明,干并不总是提高性能,有时可能导致失败.
- 这需要一种更细致的方法来阻止应用.
研究的目的:
- 建议和评估信息检索系统的选择性阻断方法.
- 为了最大限度地降低因不适当的干部造成的性能下降的风险.
- 提高IR系统的稳定性和平均检索性能.
主要方法:
- 建议采用基于查询的选择性抑制方法.
- 该方法利用查询性能预测器和来自术语频率分布的新特性.
- 机器学习技术用于选择过程.
主要成果:
- 选择性干法证明了在各种查询中进行准确的选择.
- 这种方法显著降低了每次查询性能损失的风险,增加了系统的稳定性.
- 与非选择性抑制方法相比,观察到平均检索性能有系统的改善.
结论:
- 选择性源提供了一个强大的和有效的战略,以优化信息检索性能.
- 拟议的方法,利用查询性能预测器和机器学习,成功地减轻了与源源相关的风险.
- 这种方法使得各种文档集合的平均检索结果始终更好.
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