基于相关文档池建模的查询扩展

Query Expansion Based on Modeling of Relevant Documents Pool

在信息检索领域,相关反馈是提高检索性能的有效方法之一。所谓相关反馈,指用户按照一定策略从查找到的相关文档中选择一些和主题相关的词进行查询扩展的技术。本文介绍了概率模型和向量空间模型下的常用查询扩展方法,并提出了一种基于语言模型的相关反馈方法,该方法同时考虑了扩展词应该具备的两个特征,即相关性和覆盖性。在TREC测试集上对这些算法进行了比较,结果表明这种新算法在平均准确率上比传统方法有所提高。

In information retrieval, relevance feedback is an effective way to improve retrieval performance. The goal is to input user＇s judgement on previous retrieved documents, and to select some terms for query expansion using certain strategy. This paper introduces some common query expansion approaches in relevance feedback based on probability model and vector space model, then a new term selection method is introduced based on language model,which takes into account two features of axpanded terms-＂ relevance＂ and＂ coverage＂. The evaluation is conducted on the TREC Collection, which shows that our method is better than traditional ones on average precision.