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ECIR
2008
Springer

Enhancing Relevance Models with Adaptive Passage Retrieval

8 years 3 months ago
Enhancing Relevance Models with Adaptive Passage Retrieval
Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this paper, we study passage retrieval with relevance models in the language-modeling framework for document retrieval. An adaptive passage retrieval approach is proposed to document ranking based on the best passage of a document given a query. The proposed passage ranking method is applied to two relevance-based language models: the Lavrenko-Croft relevance model and our robust relevance model. Experiments are carried out with three query sets on three different collections from TREC. Our experimental results show that combining adaptive passage retrieval with relevance models (particularly the robust ...
Xiaoyan Li, Zhigang Zhu
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ECIR
Authors Xiaoyan Li, Zhigang Zhu
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