article

Temporal profiles of queries

Abstract

Documents with timestamps, such as email and news, can be placed along a timeline. The timeline for a set of documents returned in response to a query gives an indication of how documents relevant to that query are distributed in time. Examining the timeline of a query result set allows us to characterize both how temporally dependent the topic is, as well as how relevant the results are likely to be. We outline characteristic patterns in query result set timelines, and show experimentally that we can automatically classify documents into these classes. We also show that properties of the query result set timeline can help predict the mean average precision of a query. These results show that meta-features associated with a query can be combined with text retrieval techniques to improve our understanding and treatment of text search on documents with timestamps.

References

  1. Allan, J., Callan, J., Collins-Thompson, K., Croft, B., Feng, F., Fisher, D., Lafferty, J., Larkey, L., Truong, T. N., Ogilvie, P., Si, L., Strohman, T., Turtle, H., and Zhai, C. 2003. The lemur toolkit for language modeling and information retrieval. http://www-2.cs.cmu.edu/~lemur/.Google ScholarGoogle Scholar
  2. Anick, P. 2003. Using terminological feedback for web search refinement: A log-based study. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. ACM, New York, 88--95. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Chieu, H. L. and Lee, Y. K. 2004. Query based event extraction along a timeline. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2004) (Sheffield, UK) ACM, New York, 425--432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Croft, W. B. and Lafferty, J. 2003. Language Modeling for Information Retrieval. Kluwer Academic Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cronen-Townsend, S., Zhou, Y., and Croft, W. B. 2002. Predicting query performance. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002). ACM, New York, 299--306. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Diaz, F. and Jones, R. 2004. Using temporal profiles of queries for precision prediction. In Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval. ACM, New York, 18--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. He, B. and Ounis, I. 2004. Inferring query performance using pre-retrieval predictors. In Proceedings of the 11th Symposium on String Processing and Information Retrieval (SPIRE 2004) (Padova, Italy). Lecture Notes in Computer Science, Springer-Verlag, New York.Google ScholarGoogle Scholar
  8. Kleinberg, J. 2002. Bursty and hierarchical structure in streams. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002). ACM, New York, 91--101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Krovetz, R. 1993. Viewing morphology as an inference process. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1993). ACM, New York, 191--203. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Lavrenko, V. and Croft, W. B. 2001. Relevance-based language models. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2001). ACM, New York, 120--127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Li, X. and Croft, W. B. 2003. Time-based language models. In Proceedings of the 2003 ACM CIKM International Conference on Information and Knowledge Management (CIKM 2003). ACM, New York, 469--475. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Mani, I. and Wilson, G. 2000. Robust temporal processing of news. In ACL '00: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, Morristown, NJ, 69--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Manning, C. D. and Schütze, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan-Kaufmann, Franciso, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Salton, G. 1971. The SMART Retrieval System&##8212;Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River, NJ. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Swan, R. and Jensen, D. 2000. TimeMines: Constructing timelines with statistical models of word usage. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000). ACM, New York, 73--80.Google ScholarGoogle Scholar
  17. Tomokiyo, T. and Hurst, M. 2003. A language model approach to keyphrase extraction. In Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, D. M. Francis Bond, A. Korhonen, and A. Villavicencio, Eds. 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Voorhees, E. M. and Harman, D. K., Eds. 2001. TREC: Experiment and Evaluation in Information Retrieval. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Witten, I. H. and Frank, E. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kaufmann, San Francisco, CA. http://www.cs.waikato.ac.nz/ml/weka/. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Temporal profiles of queries

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!