International Symposium on String Processing and Information Retrieval

SPIRE 2015: String Processing and Information Retrieval pp 156-164 | Cite as

Temporal Query Classification at Different Granularities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9309)


In this work, we consider the problem of classifying time-sensitive queries at different temporal granularities (day, month, and year). Our approach involves performing Bayesian analysis on time intervals of interest obtained from pseudo-relevant documents. Based on the Bayesian analysis we derive several effective features which are used to train a supervised machine learning algorithm for classification. We evaluate our method on a large temporal query workload to show that we can determine the temporal class of a query with high precision.


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  1. 1.
    Berberich, K., Bedathur, S., Alonso, O., Weikum, G.: A language modeling approach for temporal information needs. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 13–25. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  2. 2.
    Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering general time-sensitive queries. IEEE Trans. Knowl. Data Eng. 24(2), 220–235 (2012)CrossRefGoogle Scholar
  3. 3.
    Gey, F., Larson, R., Kando, N., Machado, J., Sakai, T.: Ntcir-geotime overview: Evaluating geographic and temporal search. In: Proc. NTCIR-8 Workshop Meeting, pp. 147–153 (2010)Google Scholar
  4. 4.
    Gupta, D., Berberich, K.: Identifying time intervals of interest to queries. In: CIKM 2014 (2014)Google Scholar
  5. 5.
    Joho, H., Jatowt, A., Blanco, R.: NTCIR temporalia: a test collection for temporal information access research. WWW 2014, 845–850 (2014)Google Scholar
  6. 6.
    Jones, R., Diaz, F.: Temporal profiles of queries. ACM Trans. Inf. Syst. 25(3) (2007)Google Scholar
  7. 7.
    Kanhabua, N., Nguyen, T.N., Nejdl, W.: Learning to detect event-related queries for web search. TempWeb 2015 at WWW 2015 (2015)Google Scholar
  8. 8.
    Therneau, T., Atkinson, B., Ripley, B.: rpart: Recursive Partitioning and Regression Trees (2014). R package version 4.1-8Google Scholar
  9. 9.
    Verhagen, M., Mani, I., Sauri, R., Littman, J., Knippen, R., Jang, S.B., Rumshisky, A., Phillips, J., Pustejovsky, J.: Automating temporal annotation with TARSQI. In: ACL 2005 (2005)Google Scholar
  10. 10.
    Wald, A., Wolfowitz, J.: On a test whether two samples are from the same population. The Annals of Mathematical Statistics 11(2), 147–162 (1940)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Xu, L., Bedrick, E.J., Hanson, T., Restrepo, C.: A comparison of statistical tools for identifying modality in body mass distributions. Journal of Data Science 12(1), 175–196 (2014)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany
  2. 2.Saarbrücken Graduate School of Computer ScienceSaarbrückenGermany

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