Retrieving relevant documents from a large set using the original query is a formidable challenge. A generic approach to improve the retrieval process is realized using pseudo-relevance feedback techniques. This technique allows the expansion of original queries with conducive keywords that returns the most relevant documents corresponding to the original query. In this paper, five different hybrid techniques were tested utilizing traditional query expansion methods. Later, the boosting query term method was proposed to reweigh and strengthen the original query. The query-wise analysis revealed that the proposed approach effectively identified the most relevant keywords, and that was true even for short queries. All the proposed methods’ potency was evaluated on three different datasets; Roshni, Hamshahri1, and FIRE2011. Compared to the traditional query expansion methods, the proposed methods improved the mean average precision values of Urdu, Persian, and English datasets by 14.02%, 9.93%, and 6.60%, respectively. The obtained results were also established using analysis of variance and post-hoc analysis.

使用原始查询从大量集中检索相关文档是一个巨大的挑战。使用伪相关反馈技术实现了一种改进检索过程的通用方法。此技术允许使用有益关键字扩展原始查询，该关键字将返回与原始查询相对应的最相关文档。在本文中，使用传统的查询扩展方法测试了五种不同的混合技术。后来，提出了提升查询词的方法来重新称重并增强原始查询。基于查询的分析表明，所提出的方法可以有效地识别最相关的关键字，即使是简短查询也是如此。在三个不同的数据集上评估了所有提出的方法的效力。Roshni，Hamshahri1和FIRE2011。与传统的查询扩展方法相比，该方法将Urdu，波斯和英语数据集的平均平均精度值分别提高了14.02％，9.93％和6.60％。还使用方差分析和事后分析确定了获得的结果。