情报科学 ›› 2021, Vol. 39 ›› Issue (12): 39-45.

• 理论研究 • 上一篇    下一篇

面向查询意图歧义性的多样化检索模型研究

  

  • 出版日期:2021-12-01 发布日期:2021-12-29

  • Online:2021-12-01 Published:2021-12-29

摘要: 【目的/意义】查询意图歧义性对检索模型提出了挑战。针对查询意图歧义性程度,探讨了基于歧义程度的
多样化检索模型的检索效果。【方法/过程】将查询意图歧义性程度的表示方式分为序数变量或连续变量两种方式,
在此基础上,提出了基于三种排序策略的面向序数变量查询意图歧义性的多样化检索模型、基于查询重构的面向
连续变量查询意图歧义性的多样化检索模型,从而使得检索结果列表同时具有较高的覆盖率与新颖性。【结果/结
论】在公开数据集上,四个检索效果测评指标 α-nDCG@5、α-nDCG@10、α-nDCG@20 及 NRBP@20 表明,本文
提出的多样化检索模型优于基准实验,且获取准确的查询子主题能有效提升检索效果。【创新/局限】区分了查询意
图歧义性程度的两种表示方式,据此提出并验证了面向查询意图歧义性程度的多样化检索模型;然而限于实验运
行复杂程度,生成初始检索结果列表数据略少。

Abstract: 【Purpose/significance】Query intent of ambiguity challenges retrieval models.This paper discusses the effectiveness of diver?
sity search for dealing query intent with different ambiguities【. Method/process】This paper first argues that the query ambiguity can be measured by ordinal variables or continuous variables.Later,we propose a diversity search model to deal with ordinal query ambiguity through three rerank strategies and a diversity search model to deal with continuous query ambiguity through query reformulation.Fi? nally,a diversified search result list is returned with both high coverage and novelty【. Result/conclusion】On a open access dataset,α-nDCG@5,α-nDCG@10,α-nDCG@20 and NRBP@20 show that,our proposed models outperform baseline and gathering accurate sub- topics can effectively improve the effectiveness of the retrieval model【. Innovation/limitation】Our diversified retrieval models for deal-ing with ordinal or continuous query are proposed and validated.However,due to the experiment complexity,the initial retrieval result list is slightly small.