An empirical study on query expansion and documentexpans ion in information retrieval

Query expansion (QE) and document expansion (DE) have been proved effective for improving the retrieval performance in language modeling approach. However, the issue that which expansion technique is more effective in information retrieval (IR), has not been well studied and discussed. To address th...

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Bibliographic Details
Main Authors: Xu-Wei Li, Mu-Yun Yang, Hao-Liang Qi, Sheng Li, Zhong-Yuan Han
Format: Conference Proceeding
Language:English
Subjects:
Abstracts
Adaptation models
Computational modeling
Document expansion
Information retrieval
Interpolation
Language model
Pseudo-relevance feedback
Query expansion
Online Access:Request full text
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Summary:Query expansion (QE) and document expansion (DE) have been proved effective for improving the retrieval performance in language modeling approach. However, the issue that which expansion technique is more effective in information retrieval (IR), has not been well studied and discussed. To address this issue, this paper performs an empirical study on QE and DE to examine their effects. Moreover, since QE and DE exploit different corpus structures, we also examine the potential effectiveness of incorporating QE and DE. Experimental results on several TREC test collections show that both QE and DE significantly outperform the classical language model, but the effectiveness of QE and DE is varied in different settings of retrieval. In addition, incorporating QE with DE does not always bring about the best performance.
ISSN:2160-133X
DOI:10.1109/ICMLC.2012.6359677