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An empirical study of query expansion and cluster-based retrieval in language modeling approach

机译:语言建模方法中查询扩展和基于聚类的检索的实证研究

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摘要

The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC. (c) 2006 Elsevier Ltd. All rights reserved.
机译:信息检索中的术语“不匹配问题”是一个关键问题,已经开发了多种技术来解决此问题,例如查询扩展,基于聚类的检索和降维。在这些技术中,本文对查询扩展和基于集群的检索进行了实证研究。我们研究了在查询扩展中使用简约方法的效果以及在基于集群的检索中使用聚类算法的效果。此外,通过对NTCIR和TREC的七个测试集合进行实验,比较了查询扩展和基于集群的检索,并根据检索性能评估了它们的组合。 (c)2006 Elsevier Ltd.保留所有权利。

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