Abstract
We are interested in this paper in revisiting the Divergence from Randomness (DFR) approach to Information Retrieval (IR), so as to better understand the different contributions it relies on, and thus be able to simplify it. To do so, we first introduce an analytical characterization of heuristic retrieval constraints and review several DFR models wrt this characterization. This review shows that the first normalization principle of DFR is necessary to make the model compliant with retrieval constraints. We then show that the log-logistic distribution can be used to derive a simplified DFR model. Interestingly, this simplified model contains Language Models (LM) with Jelinek-Mercer smoothing. The relation we propose here is, to our knowledge, the first connection between the DFR and LM approaches. Lastly, we present experimental results obtained on several standard collections which validate the simplification and the model we propose.
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References
Airoldi, E.M., Cohen, W.W., Fienberg, S.E.: Bayesian methods for frequent terms in text: Models of contagion and the δ² statistic
Amati, G., Rijsbergen, C.J.V.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20(4), 357–389 (2002)
Church, K.W., Gale, W.A.: Poisson mixtures. Natural Language Engineering 1, 163–190 (1995)
Clinchant, S., Gaussier, É.: The BNB distribution for text modeling. In: Macdonald, et al. (eds.) [7], pp. 150–161.
Airoldi, S.F.E.M., Cohen, W.W.: Statistical models for frequent terms in text. In: CMU-CLAD Technical Report - http://reports-archive.adm.cs.cmu.edu/cald2005.html (2004)
Fang, H., Tao, T., Zhai, C.: A formal study of information retrieval heuristics. In: SIGIR 2004: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (2004)
Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W.: ECIR 2008. LNCS, vol. 4956. Springer, Heidelberg (2008)
Na, S.-H., Kang, I.-S., Lee, J.-H.: Improving term frequency normalization for multi-topical documents and application to language modeling approaches. In: Macdonald, et al. (eds.) [7], pp. 382–393.
Singhal, A., Buckley, C., Mitra, M.: Pivoted document length normalization. In: SIGIR 1996: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 21–29. ACM, New York (1996)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004)
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Clinchant, S., Gaussier, E. (2009). Bridging Language Modeling and Divergence from Randomness Models: A Log-Logistic Model for IR. 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_6
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DOI: https://doi.org/10.1007/978-3-642-04417-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04416-8
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