Abstract
We review the history of modeling score distributions, focusing on the mixture of normal-exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most-likely universal model, with the normal-exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being “friendly” to the normal-exponential, and that the non-convexity problem that the mixture possesses is practically not severe.
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
Robertson, S.: On score distributions and relevance. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 40–51. Springer, Heidelberg (2007)
Nottelmann, H., Fuhr, N.: From uncertain inference to probability of relevance for advanced IR applications. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 235–250. Springer, Heidelberg (2003)
Callan, J.: Distributed information retrieval. In: Advances Information Retrieval: Recent Research from the CIIR, pp. 127–150. Kluwer Academic Publishers, Dordrecht (2000)
Lewis, D.D.: Evaluating and optimizing autonomous text classification systems. In: Proceedings SIGIR 1995, pp. 246–254. ACM Press, New York (1995)
Oard, D.W., Hedin, B., Tomlinson, S., Baron, J.R.: Overview of the TREC 2008 legal track. In: Proceedings TREC 2008 (2009)
Lee, J.H.: Analyses of multiple evidence combination. In: Proceedings SIGIR 1997, pp. 267–276. ACM Press, New York (1997)
Manmatha, R., Rath, T.M., Feng, F.: Modeling score distributions for combining the outputs of search engines. In: Proceedings SIGIR 2001, pp. 267–275. ACM Press, New York (2001)
Fernández, M., Vallet, D., Castells, P.: Using historical data to enhance rank aggregation. In: Proceedings SIGIR 2006, pp. 643–644. ACM Press, New York (2006)
Arampatzis, A., Beney, J., Koster, C.H.A., van der Weide, T.P.: Incrementality, half-life, and threshold optimization for adaptive document filtering. In: Proceeding TREC 2000 (2000)
Zhang, Y., Callan, J.: Maximum likelihood estimation for filtering thresholds. In: Proceedings SIGIR 2001, pp. 294–302. ACM Press, New York (2001)
Collins-Thompson, K., Ogilvie, P., Zhang, Y., Callan, J.: Information filtering, novelty detection, and named-page finding. In: Proceedings TREC 2002 (2002)
Arampatzis, A., Robertson, S., Kamps, J.: Where to stop reading a ranked list? threshold optimization using truncated score distributions. In: Proceedings SIGIR 2009. ACM Press, New York (2009)
Swets, J.A.: Information retrieval systems. Science 141(3577), 245–250 (1963)
Swets, J.A.: Effectiveness of information retrieval methods. American Documentation 20, 72–89 (1969)
Bookstein, A.: When the most “pertinent” document should not be retrieved – an analysis of the Swets model. Information Processing and Management 13(6), 377–383 (1977)
Baumgarten, C.: A probabilitstic solution to the selection and fusion problem in distributed information retrieval. In: Proceedings SIGIR 1999, pp. 246–253. ACM Press, New York (1999)
Arampatzis, A., van Hameren, A.: The score-distributional threshold optimization for adaptive binary classification tasks. In: Proceedings SIGIR 2001, pp. 285–293. ACM Press, New York (2001)
Fernández, M., Vallet, D., Castells, P.: Probabilistic score normalization for rank aggregation. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 553–556. Springer, Heidelberg (2006)
van Rijsbergen, C.J.: Information Retrieval, Butterworth (1979)
Cooper, W.S.: Some inconsistencies and misnomers in probabilistic information retrieval. In: Proceedings SIGIR 1991, pp. 57–61. ACM Press, New York (1991)
Cooper, W.S., Gey, F.C., Dabney, D.P.: Probabilistic retrieval based on staged logistic regression. In: Proceedings SIGIR 1992, pp. 198–210. ACM Press, New York (1992)
Arampatzis, A.: Unbiased s-d threshold optimization, initial query degradation, decay, and incrementality, for adaptive document filtering. In: Proceedings TREC 2001 (2002)
Robertson, S.E.: The parametric description of retrieval tests. part 1: The basic parameters. Journal of Documentation 25(1), 1–27 (1969)
Robertson, S.E., Bovey, J.D.: Statistical problems in the application of probabilistic models to information retrieval. Technical Report Report No. 5739, BLR&DD (1982)
Arampatzis, A., Kamps, J.: Where to stop reading a ranked list? In: Proceedings TREC 2008 (2008)
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
Arampatzis, A., Robertson, S., Kamps, J. (2009). Score Distributions in Information Retrieval. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04417-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-04417-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04416-8
Online ISBN: 978-3-642-04417-5
eBook Packages: Computer ScienceComputer Science (R0)