Query Expansion (QE) is widely applied to improve the retrieval performance of ad-hoc search, using different techniques and several data sources to find expansion terms. In Information Retrieval literature, selecting expansion terms remains a challenging task that relies on the extraction of term relationships. In this paper, we propose a new learning to rank-based query expansion model. The main idea behind is that, given a query and the set of its related ARs, our model ranks these ARs according to their relevance score regarding to this query and then selects the most suitable ones to be used in the QE process. Experiments are conducted on three test collections, namely: CLEF2003, TREC-Robust and TREC-Microblog, including long, hard and short queries. Results showed that the retrieval performance can be significantly improved when the ARs ranking method is used compared to other state of the art expansion models, especially for hard and long queries.

中文翻译：

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LTR-expand：基于学习对关联规则进行排序的查询扩展模型

查询扩展 (QE) 被广泛应用于提高临时搜索的检索性能，使用不同的技术和多个数据源来查找扩展项。在信息检索文献中，选择扩展术语仍然是一项具有挑战性的任务，它依赖于术语关系的提取。在本文中，我们提出了一种新的学习基于等级的查询扩展模型。背后的主要思想是，给定一个查询及其相关 AR 的集合，我们的模型根据这些 AR 与该查询的相关性得分对它们进行排名，然后选择最适合在 QE 过程中使用的那些。在三个测试集上进行了实验，即：CLEF2003、TREC-Robust 和 TREC-Microblog，包括长查询、硬查询和短查询。