Abstract
We present a Semi-supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled examples composed of queries and relevance judgments on a subset of the document elements. Our model improves the performance of a baseline Information Retrieval system by optimizing a ranking loss criterion and combining scores computed from doxels and from their local structural context. We analyze the performance of our supervised and semi-supervised algorithms on CO-Focussed and CO-Thourough tasks using a baseline model which is an adaptation of Okapi to Structured Information Retrieval.
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
Amini, M.R., Usunier, N., Gallinari, P.: Automatic text summarization based on word-clusters and ranking algorithms. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 142–156. Springer, Heidelberg (2005)
Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10, The MIT Press, Cambridge, MA (1998)
Bartell, B.T., Cottrell, G.W., Belew, R.K.: Automatic combination of multiple ranked retrieval systems. In: Research and Development in Information Retrieval, pp. 173–181 (1994)
Clémençon, S., Lugosi, G., Vayatis, N.: Ranking and scoring using empirical risk minimization. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 1–15. Springer, Heidelberg (2005)
Craswell, N., Robertson, S., Zaragoza, H., Taylor, M.: Relevance weighting for query independent evidence. In: SIGIR 2005. Proceedings of the 28th annual international ACM SIGIR conference, ACM Press, New York (2005)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via EM algorithm. Journal of the Royal Statistical Society B(39), 1–38 (1977)
Denoyer, L., Gallinari, P.: The Wikipedia XML Corpus SIGIR Forum (2006)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. In: Proceedings of ICML 1998. 15th International Conference on Machine Learning (1998)
Miller, D., Uyar, H.: A Mixture of Experts classifier with learning based on both labeled and unlabeled data. Advances in Neural Information Processing Systems 9, 571–577 (1996)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text Classification from Labeled and Unlabeled Documents using EM. In: Proceedings of National Conference on Artificial Intel-ligence (1998)
Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at TREC. In: Text REtrieval Conference, pp. 21–30 ( 1992)
Vittaut, J.N., Piwowarski, B., Gallinari, P.: An algebra for structured queries in bayesian networks. In: Fuhr, N., Lalmas, M., Malik, S., Szlávik, Z. (eds.) INEX 2004. LNCS, vol. 3493, Springer, Heidelberg (2005)
Vittaut, J.N., Amini, M.R., Gallinari, P.: Learning Classification with Both Labeled and Unlabeled Data. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vittaut, JN., Gallinari, P. (2007). Supervised and Semi-supervised Machine Learning Ranking. In: Fuhr, N., Lalmas, M., Trotman, A. (eds) Comparative Evaluation of XML Information Retrieval Systems. INEX 2006. Lecture Notes in Computer Science, vol 4518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73888-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-73888-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73887-9
Online ISBN: 978-3-540-73888-6
eBook Packages: Computer ScienceComputer Science (R0)