Abstract
Query expansion is an effective technique in improving the retrieval performance for ad-hoc retrieval. However, query expansion can also fail, leading to a degradation of the retrieval performance. In this paper, we aim to provide a better understanding of query expansion by an empirical study on what factors can affect query expansion, and how these factors affect query expansion. We examine how the quality of the query, measured by the first-pass retrieval performance, is related to the effectiveness of query expansion. Our experimental results only show a moderate relation between them, indicating that the first-pass retrieval has only a moderate impact on the effectiveness of query expansion. Our results also show that the feedback documents should not only be relevant, but should also have a dedicated interest in the topic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amati, G.: Probabilistic models for information retrieval based on divergence from randomness. PhD thesis, Department of Computing Science, University of Glasgow (2003)
Amati, G., Carpineto, C., Romano, G.: Query Difficulty, Robustness, and Selective Application of Query Expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)
Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C., Gambosi, G.: FUB, IASI-CNR and University of Tor Vergata at TREC 2007 Blog Track. In: Proceedings of TREC 2007 (2007)
Cao, G., Nie, J., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of SIGIR 2008 (2008)
Carpineto, C., de Mori, R., Romano, G., Bigi, B.: An information-theoretic approach to automatic query expansion. ACM Transactions on Information Systems 19(1) (2001)
Carpineto, C., Romano, G., Gianini, V.: Improving retrieval feedback with multiple term-ranking function combination. ACM Transactions on Information Systems 20(3) (2002)
He, B., Macdonald, C., Ounis, I., Peng, J., Santos, R.L.T.: University of Glasgow at TREC 2008: Experiments in Blog, Enterprise, and Relevance Feedback Tracks with Terrier. In: Proceedings of TREC 2008 (2008)
Kwok, K., Grunfeld, L., Sun, H., Deng, P.: TREC 2004 Robust Track Experiments Using PIRCS. In: Proceedings of TREC 2004 (2004)
Macdonald, C., Ounis, I.: Expertise drift and query expansion in expert search. In: Proceedings of CIKM 2007 (2007)
Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Lioma, C.: Terrier: A high performance and scalable judgements retrieval platform. In: Proceedings of the OSIR Workshop (2006)
Robertson, S.E., Walker, S., Beaulieu, M.M., Gatford, M., Payne, A.: Okapi at TREC-4. In: Proceedings of TREC 4 (1995)
Rocchio, J.: Relevance feedback in information retrieval, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)
Voorhees, E.: TREC: Experiment and Evaluation in Information Retrieval. MIT Press, Cambridge (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, B., Ounis, I. (2009). Studying Query Expansion Effectiveness. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-00958-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00957-0
Online ISBN: 978-3-642-00958-7
eBook Packages: Computer ScienceComputer Science (R0)