Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different configurations, starting from methods based on association rules and combining it with external knowledge. The HQE model handles the two main phases of a QE process, namely: the candidate terms generation phase and the selection phase. We propose for the first phase, statistical, semantic and conceptual methods to generate new related terms for a given query. For the second phase, we introduce a similarity measure, ESAC, based on the Explicit Semantic Analysis that computes the relatedness between a query and the set of candidate terms. The performance of the proposed HQE model is evaluated within two experimental validations. The first one addresses the tweet search task proposed by TREC Microblog Track 2011 and an ad-hoc IR task related to the hard topics of the TREC Robust 2004. The second experimental validation concerns the tweet contextualization task organized by INEX 2014. Global results highlighted the effectiveness of our HQE model and of association rules mining for QE combined with external resources.

中文翻译：

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文本和微博信息检索的混合查询扩展模型

查询扩展（QE）是信息检索应用程序中的一个重要过程，它可以改善用户查询并有助于检索相关结果。在本文中，我们介绍了一种混合查询扩展模型（HQE），该模型研究如何将外部资源组合到关联规则挖掘中，并用于增强扩展术语的生成和选择。HQE模型可以以不同的配置进行处理，从基于关联规则的方法开始，并将其与外部知识相结合。HQE模型处理QE流程的两个主要阶段，即：候选词生成阶段和选择阶段。我们建议在第一阶段使用统计，语义和概念方法为给定查询生成新的相关术语。在第二阶段，我们引入了一种相似性度量ESAC，基于显式语义分析的，该显式语义分析计算查询与候选词集之间的相关性。建议的HQE模型的性能在两次实验验证中得到了评估。第一个解决方案是TREC Microblog Track 2011提出的推文搜索任务，以及与TREC Robust 2004的硬性主题相关的临时IR任务。第二个实验验证涉及INEX 2014组织的推文上下文化任务。 HQE模型的有效性以及结合外部资源进行QE的关联规则挖掘的有效性。