Promoting Divergent Terms in the Estimation of Relevance Models
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Abstract
Traditionally the use of pseudo relevance feedback (PRF) techniques for query expansion has been demonstrated very effective. Particularly the use of Relevance Models (RM) in the context of the Language Modelling framework has been established as a high-performance approach to beat. In this paper we present an alternative estimation for the RM promoting terms that being present in the relevance set are also distant from the language model of the collection. We compared this approach with RM3 and with an adaptation to the Language Modelling framework of the Rocchio’s KLD-based term ranking function. The evaluation showed that this alternative estimation of RM reports consistently better results than RM3, showing in average to be the most stable across collections in terms of robustness.
Keywords
Language Modelling Relevance Feedback Query Expansion Mean Average Precision Divergent TermPreview
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References
- 1.Abdul-Jaleel, N., Allan, J., Croft, W.B., Diaz, O., Larkey, L., Li, X., Smucker, M.D., Wade, C.: UMass at trec 2004: Novelty and hard. In: Proceedings of TREC-13 (2004)Google Scholar
- 2.Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 357–389 (2002)CrossRefGoogle Scholar
- 3.Balasubramanian, N., Allan, J., Croft, W.B.: A comparison of sentence retrieval techniques. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 813–814. ACM, New York (2007)Google Scholar
- 4.Carpineto, C., de Mori, R., Romano, G., Bigi, B.: An information-theoretic approach to automatic query expansion. ACM Trans. Inf. Syst. 19(1), 1–27 (2001)CrossRefGoogle Scholar
- 5.Collins-Thompson, K., Callan, J.: Estimation and use of uncertainty in pseudo-relevance feedback. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 303–310. ACM, New York (2007)Google Scholar
- 6.Croft, W.B., Harper, D.: Using probabilistic models of document retrieval without relevance information. Journal of Documentation 35, 285–295 (1979)CrossRefGoogle Scholar
- 7.Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice, 1st edn. Addison-Wesley Publishing Company, USA (2009)Google Scholar
- 8.Doszkocs, T.: Id, an associative interactive dictionary for online searching. Online Review 2, 163–173 (1978)CrossRefGoogle Scholar
- 9.He, B., Ounis, I.: Combining fields for query expansion and adaptive query expansion. Inf. Process. Manage. 43, 1294–1307 (2007)CrossRefGoogle Scholar
- 10.Lavrenko, V., Croft, W.B.: Relevance based language models. In: SIGIR 2001: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 120–127. ACM, New York (2001)Google Scholar
- 11.Lee, K.S., Croft, W.B., Allan, J.: A cluster-based resampling method for pseudo-relevance feedback. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235–242. ACM, New York (2008)Google Scholar
- 12.Li, X., Zhu, Z.: Enhancing relevance models with adaptive passage retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 463–471. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 13.Lv, Y., Zhai, C.: Adaptive relevance feedback in information retrieval. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 255–264. ACM, New York (2009)Google Scholar
- 14.Lv, Y., Zhai, C.: A comparative study of methods for estimating query language models with pseudo feedback. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1895–1898. ACM, New York (2009)Google Scholar
- 15.Robertson, S.E.: On term selection for query expansion. J. Doc. 46, 359–364 (1991)CrossRefGoogle Scholar
- 16.Rocchio, J.: Relevance feedback in information retrieval. In: The SMART Retrieval System, pp. 313–323 (1971)Google Scholar
- 17.Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev. 18(2), 95–145 (2003)CrossRefGoogle Scholar
- 18.Sakai, T., Manabe, T., Koyama, M.: Flexible pseudo-relevance feedback via selective sampling. ACM Transactions on Asian Language Information Processing (TALIP) 4(2), 111–135 (2005)CrossRefGoogle Scholar
- 19.Salton, G.: The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River (1971)Google Scholar
- 20.Tao, T., Zhai, C.: Regularized estimation of mixture models for robust pseudo-relevance feedback. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 162–169. ACM, New York (2006)Google Scholar
- 21.Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: SIGIR 1996: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 4–11. ACM, New York (1996)CrossRefGoogle Scholar
- 22.Ye, Z., He, B., Huang, X., Lin, H.: Revisiting rocchios relevance feedback algorithm for probabilistic models. In: Cheng, P.-J., Kan, M.-Y., Lam, W., Nakov, P. (eds.) AIRS 2010. LNCS, vol. 6458, pp. 151–161. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 23.Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, CIKM 2001, pp. 403–410. ACM, New York (2001)CrossRefGoogle Scholar
- 24.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)CrossRefGoogle Scholar