Abstract
Documents and queries are rich in temporal features, both at the meta-level and at the content-level. We exploit this information to define temporal scope similarities between documents and queries in metric spaces. Our experiments show that the proposed metrics can be very effective for modeling the relevance for different search tasks, and provide insights into an inherent asymmetry in temporal query semantics. Moreover, we propose a simple ranking model that combines the temporal scope similarity with traditional keyword similarities. We experimentally show that it is not worse than traditional keyword-based rankings for non-temporal queries, and that it improves the overall effectiveness for time-based queries.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alonso, O., Strötgen, J., Baeza-Yates, R., Gertz, M.: Temporal information retrieval: Challenges and opportunities. In: 1st Temporal Web Analytics Workshop at WWW, pp. 1–8 (2011)
Jones, R., Diaz, F.: Temporal profiles of queries. ACM Transactions on Information Systems (TOIS) 25(3) (2007)
Campos, R., Dias, G., Jorge, A.M., Nunes, C.: Enriching temporal query understanding through date identification: how to tag implicit temporal queries? In: Proceedings of the 2nd Temporal Web Analytics Workshop, pp. 41–48. ACM (2012)
Berberich, K., Bedathur, S., Alonso, O., Weikum, G.: A language modeling approach for temporal information needs. In: Advances in Information Retrieval, pp. 13–25 (2010)
Nunes, S., Ribeiro, C., David, G.: Use of temporal expressions in web search. In: Advances in Information Retrieval, pp. 580–584 (2008)
Snodgrass, R.T.: Temporal databases. IEEE Computer 19, 35–42 (1986)
Li, X., Croft, W.: Time-based language models. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 469–475. ACM (2003)
Alonso, O., Gertz, M., Baeza-Yates, R.: On the value of temporal information in information retrieval. In: ACM SIGIR Forum, vol. 41, pp. 35–41. ACM (2007)
Verhagen, M., Gaizauskas, R., Schilder, F., Hepple, M., Moszkowicz, J., Pustejovsky, J.: The tempeval challenge: identifying temporal relations in text. Language Resources and Evaluation 43(2), 161–179 (2009)
Strötgen, J., Gertz, M.: Heideltime: High quality rule-based extraction and normalization of temporal expressions. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 321–324. Association for Computational Linguistics, Uppsala (2010)
Llorens, H., Derczynski, L., Gaizauskas, R., Saquete, E.: Timen: An open temporal expression normalisation resource. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (2012)
Metzler, D., Jones, R., Peng, F., Zhang, R.: Improving search relevance for implicitly temporal queries. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 700–701. ACM (2009)
Kanhabua, N., Nørvåg, K.: Determining time of queries for re-ranking search results. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 261–272. Springer, Heidelberg (2010)
Gey, F., Larson, R., Kando, N., Machado, J., Sakai, T.: Ntcir-geotime overview: Evaluating geographic and temporal search. In: NTCIR, vol. 10, pp. 147–153 (2010)
Diaz, F., Dumais, S., Efron, M., Radinsky, K., de Rijke, M., Shokouhi, M.: Sigir 2013 workshop on time aware information access (# taia2013). In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1137–1137. ACM (2013)
Nunes, S.: Exploring temporal evidence in web information retrieval. In: Future Directions in Information Access (FDIA) (2007)
Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Sibson, R.: Slink: an optimally efficient algorithm for the single-link cluster method. The Computer Journal 16(1), 30–34 (1973)
Black, P.E.: Manhattan distance. Dictionary of algorithms and data structures. US National Institute of Standards and Technology (2006)
Shepard, R.N., et al.: Toward a universal law of generalization for psychological science. Science 237(4820), 1317–1323 (1987)
Pustejovsky, J., Castano, J., Ingria, R., Saurí, R., Gaizauskas, R., Setzer, A., Katz, G., Radev, D.: TimeML: Robust specification of event and temporal expressions in text. In: Mani, I., Pustejovsky, J., Gaizauskas, R. (eds.) The Language of time: a Reader. Oxford University Press (2005)
Sakai, T.: Evaluating information retrieval metrics based on bootstrap hypothesis tests. Information and Media Technologies 2(4), 1062–1079 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Brucato, M., Montesi, D. (2014). Metric Spaces for Temporal Information Retrieval. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-06028-6_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06027-9
Online ISBN: 978-3-319-06028-6
eBook Packages: Computer ScienceComputer Science (R0)