Abstract
Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user’s manual judgment of those documents would much help to select good expansion terms, it is difficult to get enough feedback from users in practical situations. In this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document. In order to tackle this specific condition, we introduce two refinements to a well-known query expansion technique. One is to increase documents possibly being relevant by a transductive learning method because the more relevant documents will produce the better performance. The other is a modified term scoring scheme based on the results of the learning method and a simple function. Experimental results show that our technique outperforms some traditional methods in standard precision and recall criteria.
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
Ruthven, I.: Re-examining the potential effectiveness of interactive query expansion. In: Proceedings of SIGIR 2003, pp. 213–220 (2003)
Dumais, S., et al.: Sigir 2003 workshop report: Implicit measures of user interests and preferences. In: SIGIR Forum (2003)
Yu, S., et al.: Improving pseud-relevance feedback in web information retrieval using web page segmentation. In: Proceedings of WWW 2003 (2003)
Lam-Adesina, A.M., Jones, G.J.F.: Applying summarization techniques for term selection in relevance feedback. In: Proceedings of SIGIR 2001, pp. 1–9 (2001)
Onoda, T., Murata, H., Yamada, S.: Non-relevance feedback document retrieva. In: Proceedings of CIS 2004. IEEE, Los Alamitos (2003)
He, J., et al.: Manifold-ranking based image retrieval. In: Proceedings of Multimedia 2004, pp. 9–13. ACM, New York (2004)
Flake, G.W., et al.: Extracting query modification from nonlinear svms. In: Proceedings of WWW 2002 (2002)
Oyama, S., et al.: keysword spices: A new method for building domain-specific web search engines. In: Proceedings of IJCAI 2001 (2001)
Robertson, S.E.: On term selection for query expansion. Journal of Documentation 46(4), 359–364 (1990)
Robertson, S.E.: Overview of the okapi projects. Journal of the American Society for Information Science 53(1), 3–7 (1997)
Vapnik, V.: Statistical learning theory. Wiley, Chichester (1998)
Joachims, T.: Transductive learning via spectral graph partitioning. In: Proceedings of ICML 2003, pp. 143–151 (2003)
Zhu, X., et al.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of ICML 2003, pp. 912–914 (2003)
Blum, A., et al.: Semi-supervised learning using randomized mincuts. In: Proceedings of ICML 2004 (2004)
Voorhees, E., Harman, D.: Overview of the eighth text retrieval conference (1999)
Aalbersberg, I.J.: Incremental relevance feedback. In: Proceedings of SIGIR 1992, pp. 11–22 (1992)
Allan, J.: Incremental relevance feedback for information filtering. In: Proceedings of SIGIR 1996, pp. 270–278 (1996)
Iwayama, M.: Relevance feedback with a small number of relevance judgements: Incremental relevance feedback vs. document clustering. In: Proceedings of SIGIR 2000, pp. 10–16 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okabe, M., Umemura, K., Yamada, S. (2005). Query Expansion with the Minimum Relevance Judgments. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_3
Download citation
DOI: https://doi.org/10.1007/11562382_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29186-2
Online ISBN: 978-3-540-32001-2
eBook Packages: Computer ScienceComputer Science (R0)