Abstract
The prediction of query performance is an interesting and important issue in Information Retrieval (IR). Current predictors involve the use of relevance scores, which are time-consuming to compute. Therefore, current predictors are not very suitable for practical applications. In this paper, we study a set of predictors of query performance, which can be generated prior to the retrieval process. The linear and non-parametric correlations of the predictors with query performance are thoroughly assessed on the TREC disk4 and disk5 (minus CR) collections. According to the results, some of the proposed predictors have significant correlation with query performance, showing that these predictors can be useful to infer query performance in practical applications.
Keywords
- Average Precision
- Query Term
- Query Expansion
- Information Retrieval System
- Relevance Score
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
Allan, J., Ballesteros, L., Callan, J., Croft, W.: Recent experiments with INQUERY. In: Proceedings of TREC-4, Gaithersburg, MD, pp. 49–63 (1995)
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., van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. TOIS 20(4), 357–389 (2002)
Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of SIGIR 2002,Tampere, Finland, pp. 299–306 (2002)
DeGroot, M.: Probability and Statistics, 2nd edn. Addison Wesley, Reading (1989)
Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. M. Dekker, New York (1992)
He, B., Ounis, I.: A study of parameter tuning for term frequency normalization. In: Proceedings of CIKM 2003, New Orleans, LA, pp. 10–16 (2003)
He, B., Ounis, I.: A query-based pre-retrieval model selection approach to information retrieval. In: Proceedings of RIAO 2004, Avignon, France, pp. 706–719 (2004)
Pirkola, A., Jarvelin, K.: Employing the resolution power of search keys. JASIST 52(7), 575–583 (2001)
Plachouras, V., Ounis, I., Amati, G., van Rijsbergen, C.J.: University of Glasgow at the Web Track: Dynamic application of hyperlink analysis using the query scope. In: Proceedings of TREC 2003, Gaithersburg, MD, pp. 248–254 (2003)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of SIGIR 1998, Melbourne, Australia, pp. 275–281 (1998)
Robertson, S., Walker, S., Beaulieu, M.M., Gatford, M., Payne, A.: Okapi at TREC-4. In: Proceedings of TREC-4, Gaithersburg, MD, pp. 73–96 (1995)
Song, F., Croft, W.: A general language model for information retrieval. In: Proceedings of SIGIR 1999, Berkeley, CA, pp. 279–280 (1999)
Sparck-Jones, K., Walker, S., Robertson, S.: A probabilistic model of information retrieval: Development and comparative experiments. IPM 36(2000), 779–840 (2000)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of SIGIR 2001, New Orleans, LA, pp. 334–342 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, B., Ounis, I. (2004). Inferring Query Performance Using Pre-retrieval Predictors. In: Apostolico, A., Melucci, M. (eds) String Processing and Information Retrieval. SPIRE 2004. Lecture Notes in Computer Science, vol 3246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30213-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-30213-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23210-0
Online ISBN: 978-3-540-30213-1
eBook Packages: Springer Book Archive