Skip to main content

Experiments on Pseudo Relevance Feedback Using Graph Random Walks

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7608)

Abstract

In this article, we apply a graph-based approach for pseudo-relevance feedback. We model term co-occurrences in a fixed window or at the document level as a graph and apply a random walk algorithm to select expansion terms. Evaluation of the proposed approach on several standard TREC and CLEF collections including the recent TREC-Microblog dataset show that this approach is in line with state-of-the-art pseudo-relevance feedback models.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agirre, E., Soroa, A.: Personalizing PageRank for word sense disambiguation. In: Proceedings of the 12th EACL Conference, pp. 33–41 (2009)

    Google Scholar 

  2. Amati, G.: Probability Models for Information Retrieval based on Divergence from Randomness. Ph.D. thesis, Department of Computing Science University of Glasgow (2003)

    Google Scholar 

  3. Amati, G., Carpineto, C., Romano, G.: Query Difficulty, Robustness, and Selective Application of Query Expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  4. Blanco, R., Lioma, C.: Random walk term weighting for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference, pp. 829–830 (2007)

    Google Scholar 

  5. Cavnar, W.B., Trenkle, J.M.: N-gram-based text categorization. In: Proceedings of SDAIR 1994, 3rd Annual Symposium on Document Analysis and Information Retrieval, pp. 161–175 (1994)

    Google Scholar 

  6. Collins-Thompson, K., Callan, J.: Query expansion using random walk models. In: Proceedings of the 14th CIKM Conference, pp. 704–711 (2005)

    Google Scholar 

  7. de Groc, C., Tannier, X., Couto, J.: GrawlTCQ: Terminology and Corpora Building by Ranking Simultaneously Terms, Queries and Documents using Graph Random Walks. In: Proceedings of the TextGraphs-6 Workshop, pp. 37–41 (2011)

    Google Scholar 

  8. Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference, pp. 111–119. ACM (2001)

    Google Scholar 

  9. Lv, Y., Zhai, C.: Positional relevance model for pseudo-relevance feedback. In: Proceeding of the 33rd International ACM SIGIR Conference, pp. 579–586 (2010)

    Google Scholar 

  10. Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. In: Proceedings of EMNLP, pp. 404–411 (2004)

    Google Scholar 

  11. Ounis, I., Macdonald, C., Lin, J., Soboroff, I.: Overview of the trec-2011 microblog track. Tech. rep., DTIC Document (2011)

    Google Scholar 

  12. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Tech. rep., Stanford InfoLab (1999)

    Google Scholar 

  13. Ramage, D., Rafferty, A., Manning, C.: Random walks for text semantic similarity, p. 23. Association for Computational Linguistics, Morristown (2009)

    Google Scholar 

  14. Rocchio, J.: Relevance feedback in information retrieval. In: The Smart Retrieval system—experiments in Automatic Document Processing, pp. 313–323 (1971)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

de Groc, C., Tannier, X. (2012). Experiments on Pseudo Relevance Feedback Using Graph Random Walks. In: Calderón-Benavides, L., González-Caro, C., Chávez, E., Ziviani, N. (eds) String Processing and Information Retrieval. SPIRE 2012. Lecture Notes in Computer Science, vol 7608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34109-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34109-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34108-3

  • Online ISBN: 978-3-642-34109-0

  • eBook Packages: Computer ScienceComputer Science (R0)