Advertisement

A Study on Query Expansion Based on Topic Distributions of Retrieved Documents

  • Midori Serizawa
  • Ichiro Kobayashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)

Abstract

This paper describes a new relevance feedback (RF) method that uses latent topic information extracted from target documents.In the method, we extract latent topics of the target documents by means of latent Dirichlet allocation (LDA) and expand the initial query by providing the topic distributions of the documents retrieved at the first search. We conduct experiments for retrieving information by our proposed method and confirm that our proposed method is especially useful when the precision of the first search is low. Furthermore, we discuss the cases where RF based on latent topic information and RF based on surface information, i.e., word frequency, work well, respectively.

Keywords

Information Retrieval Latent Dirichlet Allocation Relevance Feedback Feedback Information Query Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rocchio, J.J.: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall Inc. (1971)Google Scholar
  2. 2.
    Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: Proceedings of SIGIR 2001, pp. 111–119 (2001)Google Scholar
  3. 3.
    Carpineto, C., de Mori, R., Romano, G., Bigi, B.: An informationtheoretic approach to automatic query expansion. ACM Transactions on Information Systems (TOIS) 19, 1–27 (2001)CrossRefGoogle Scholar
  4. 4.
    Lavrenko, V., Croft, W.B.: Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2001), pp. 120–127 (2001)Google Scholar
  5. 5.
    Collins-Thompson, K.: Reducing the risk of query expansion via robust constrained optimization. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM 2009), pp. 837–846. ACM Press, New York (2009)CrossRefGoogle Scholar
  6. 6.
    Lee, K.S., Croft, W.B., Allan, J.: A cluster-based resampling method for pseudo-relevance feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 235–242. ACM Press, New York (2008)CrossRefGoogle Scholar
  7. 7.
    He, B., Ounis, I.: Studying query expansion effectiveness. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 611–619. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 50–57. ACM Press, NewYork (1999)CrossRefGoogle Scholar
  9. 9.
    David, M., Blei, A.Y.N., Jordan, M.I.: Latent dirichlet allocation. Jounal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  10. 10.
    Wei, X., Croft, W.B.: Lda-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006), pp. 178–185. ACM Press, NewYork (2006)CrossRefGoogle Scholar
  11. 11.
    Yi, X., Allan, J.: Evaluating topic models for information retrieval. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), pp. 1431–1432. ACM Press, New York (2008)Google Scholar
  12. 12.
    Yi, X., Allan, J.: A comparative study of utilizing topic models for information retrieval. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 29–41. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Zhou, D., Wade, V.: Latent document re-ranking. In: Proceedings of EMNLP 2009, pp. 1571–1580 (2009)Google Scholar
  14. 14.
    Xu, J., Croft, W.B.: Cluster-based language models for distributed retrieval. In: Proceedings of ACM SIGIR, pp. 254–261 (1999)Google Scholar
  15. 15.
    Li, W., McCallum, A.: Pachinko allocation: Dag-structured mixture models of topic correlations. In: Proceedings of ICML, Pittsburgh, PA, pp. 577–584 (2006)Google Scholar
  16. 16.
    Ye, Z., Huang, X., Lin, H.: Finding a good query-related topic for boosting pseudo-relevance feedback. Journal of the American Society for Information Science and Technology archive 62(4), 748–760 (2011)CrossRefGoogle Scholar
  17. 17.
    Harashima, J., Kurohashi, S.: Relevance feedback using latent information. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, pp. 1037–1045 (2011)Google Scholar
  18. 18.
    Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of CIKM 2001, pp. 403–410 (2001)Google Scholar
  19. 19.
    Kando, N.: Overview of the second ntcir workshop. In: Proceedings of the Second NTCIR Workshop on Research in Chinese & Japanese Text Retrieval and Text Summarization (2000), http://research.nii.ac.jpntcirworkshopOnlineProceedings2ovview-kando.pdf
  20. 20.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems 22(2), 170–214 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Midori Serizawa
    • 1
  • Ichiro Kobayashi
    • 1
  1. 1.Advanced Sciences, Faculty of SciencesOchanomizu UniversityTokyoJapan

Personalised recommendations