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Temporal Information Retrieval and Its Application: A Survey

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Emerging Research in Computing, Information, Communication and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 906))

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Abstract

With an advent of the Web, a tremendous amount of information is available online. Information can be organized and explored in the time dimension. This temporal information has to be distilled out, so as to extract the temporal entities such as temporal expressions and temporal relations out of it. Temporal information processing is an ongoing field of research that deals with natural language text, temporal relations, events or temporal queries. This paper presents a detailed analysis of the work carried out under temporal information retrieval (TIR) highlighting its subtasks like information extraction, indexing, ranking, query processing, clustering and classification. Also, it presents various challenges while dealing with temporal information. To the end, various application areas are elaborated such as temporal summarization, exploration and future event retrieval.

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References

  1. Metzger, M. (2007). Making sense of credibility on the web: Models for evaluating online information and recommendations for future research. Journal of the American Society for Information Science and Technology, 58, 2078–2091. https://doi.org/10.1002/asi.20672.

    Article  Google Scholar 

  2. Schilder, F., & Habel, C. (2001). From temporal expressions to temporal information: Semantic tagging of news messages. In ACL01 Work Temporal Spat Information Processing (pp. 65–72). https://doi.org/10.3115/1118238.1118247.

  3. Ingria, R., Saur, R., Pustejovsky, J., et al. (2002). TimeML: Robust specification of event and temporal expressions in text. In Proceedings of the AAAI Spring Symposium on New Directions in Question Answering (pp. 28–34).

    Google Scholar 

  4. Allen, J. (1983). Maintaining knowledge about temporal intervals. Communication of the ACM, 26, 823–843. https://doi.org/10.1145/182.358434.

    Article  MATH  Google Scholar 

  5. Pustejovsky, J., Lee, K., Bunt, H., & Romary, L. (2010). ISO-TimeML: An international standard for semantic annotation. In LREC, May 2010, La Valette, Malta.

    Google Scholar 

  6. Mani, I., & Wilson, G. (2000). Robust temporal processing of news. In Association for Computational Linguistics (pp. 69–76).

    Google Scholar 

  7. Negri, M., & Marseglia, L. (2005). Recognition and normalization of time expressions: ITC-irst at TERN 2004 (Tech. Report WP3.7, Information Society Technologies).

    Google Scholar 

  8. Uzzaman, N., & Allen, J. F. (2010). TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text. In Proceedings of the 5th International Workshop on Semantic Evaluation ACL (pp. 276–283).

    Google Scholar 

  9. Chang, A. X., & Manning, C. D. (2012). SUTime: A library for recognizing and normalizing time expressions. In LREC (pp. 3735–3740).

    Google Scholar 

  10. Strötgen, J., & Gertz, M. (2010). HeidelTime high-quality rule-based extraction and normalization of temporal expressions. In Proceedings of the 5th International Workshop on Semantic Evaluation, ACL (pp. 321–324).

    Google Scholar 

  11. Strötgen, J., & Gertz, M. (2013). Multilingual and cross-domain temporal tagging. Language Resources and Evaluation, 47, 269–298. https://doi.org/10.1007/s10579-012-9179-y.

    Article  Google Scholar 

  12. Ramrakhiyani, N., & Majumder, P. (2015). Approaches to temporal expression recognition in Hindi. ACM Transactions on Asian and Low-Resource Language Information Processing, 14, 1–22. https://doi.org/10.1145/2629574.

    Article  Google Scholar 

  13. Bethard, S. (2013). ClearTK-TimeML: A minimalist approach to TempEval 2013. Seventh International Workshop on Semantic Evaluation, 2, 10–14.

    Google Scholar 

  14. Llorens, H., Saquete, E., & Navarro, B. (2010). Tipsem (English and Spanish): Evaluating crfs and semantic roles in tempeval-2. In Proceeding SemEval’10 Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 284–291).

    Google Scholar 

  15. Tang, Y., Ye, X., & Tang, N. (2011). Temporal information processing technology and its application, Chap. 8, (pp. 51–158).

    Google Scholar 

  16. Zobel, J., & Moffat, A. (2006). Inverted files for text search engines. ACM computing surveys (CSUR), 38(2), 1–56. https://doi.org/10.1145/1132956/1132959.

    Article  Google Scholar 

  17. Berberich, K., Bedathur, S., Neumann, T., & Weikum, G. (2007). A time machine for text search. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval —SIGIR’07, (pp. 519–526). https://doi.org/10.1145/1277741.1277831.

  18. Berberich, K., Bedathur, S., Neumann, T., & Weikum, G. (2007). FluxCapacitor: Efficient time-travel text search. In Proceedings of the VLDB ’07 (pp. 1414–1417).

    Google Scholar 

  19. Jin, P., Lian, J., Zhao, X., & Wan, S. (2008). TISE: A temporal search engine for web contents. In Proceedings—2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008 (pp. 220–224). https://doi.org/10.1109/iita.2008.132.

  20. Nascimento, M. A., & Dunham, M. H. (1999). Indexing valid time databases via B+-trees. IEEE Transactions on Knowledge and Data Engineering, 11, 929–947. https://doi.org/10.1109/69.824609.

    Article  Google Scholar 

  21. Anand, A., Bedathur, S., Berberich, K., & Schenkel, R. (2012). Index maintenance for time-travel text search. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR’12 (pp. 235–243). https://doi.org/10.1145/2348283.2348318.

  22. Layer, R. M., Pedersen, B. S., Disera, T., Marth, G. T., Gertz, J., & Quinlan, A. R. (2018). GIGGLE: A search engine for large-scale integrated genome analysis. Nature Methods, 15, 123–126. https://doi.org/10.1038/nmeth.4556.

    Article  Google Scholar 

  23. Li, X., & Croft, W. B. (2003). Time-based language models. In Proceedings of the 12th International Conference on Information and Knowledge Management (pp. 469–475).

    Google Scholar 

  24. Jatowt, A., Kawai, Y., & Tanaka, K. (2005). Temporal ranking of search engine results. In Proceedings of the 6th International Conference on Web Information Systems Engineering (pp. 43–52).

    Google Scholar 

  25. Zhang, R., Chang, Y., & Zheng, Z. (2009). Search result re-ranking by feedback control adjustment for time-sensitive query. In Proceedings of NAACL HLT (pp. 165–168).

    Google Scholar 

  26. Strötgen, J., & Gertz, M. (2013). Proximity 2-aware ranking for textual, temporal, and geographic queries. In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management—CIKM’13 (pp. 739–744).

    Google Scholar 

  27. Costa, M., Couto, F., & Silva, M. (2014). Learning temporal-dependent ranking models. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval—SIGIR’14 (pp. 757–766).

    Google Scholar 

  28. Cheng, S., Arvanitis, A., & Hristidis, V. (2013). How fresh do you want your search results? In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management—CIKM’13 (pp. 1271–1280). https://doi.org/10.1145/2505515.2505696.

  29. Campos, R., Dias, G., Jorge, A., & Jatowt, A. (2014). Survey of temporal information retrieval and related applications. ACM Computing Surveys, 47, 1–41. https://doi.org/10.1145/2619088.

    Article  Google Scholar 

  30. Dong, A., Chang, Y., Zheng, Z., et al. (2010). Towards recency ranking in web search. In Proceedings of the Third ACM International Conference on Web Search and Data Mining—WSDM’10 (pp. 11–20). https://doi.org/10.1145/1718487.1718490.

  31. Dakka, W., Gravano, L., & Ipeirotis, P. (2012). Answering general time-sensitive queries. IEEE Transactions on Knowledge and Data Engineering, 24(2), 220–235.

    Article  Google Scholar 

  32. Jones, R., & Diaz, F. (2007). Temporal profiles of queries. ACM Transactions on Information Systems, 25(3), 14. https://doi.org/10.1145/1247715.1247720.

    Article  Google Scholar 

  33. Alonso, O., & Gertz, M. (2006). Clustering of search results using temporal attributes. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR’06 (pp. 597–598). https://doi.org/10.1145/1148170.1148273.

  34. Zamir, O., & Etzioni, O. (1998). Web document clustering. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR’98 (pp. 46–54). https://doi.org/10.1145/290941.290956.

  35. Toda, H., & Kataoka, R. (2005). A search result clustering method using informatively named entities. In Proceedings of the Seventh ACM International Workshop on Web Information and Data Management—WIDM’05 (pp. 81–86). https://doi.org/10.1145/1097047.1097063.

  36. Jatowt, A., & Au Yeung, C. (2011). Extracting collective expectations about the future from large text collections. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management—CIKM’11 (pp. 1259–1264). https://doi.org/10.1145/2063576.2063759.

  37. Mani, I., Verhagen, M., Wellner, B., et al. (2006). Machine learning of temporal relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL—ACL’06 (pp. 753–760). https://doi.org/10.3115/1220175.1220270.

  38. Chambers, N., Wang, S., & Jurafsky, D. (2007). Classifying temporal relations between events. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (ACL’07), Association for Computational Linguistics, Stroudsburg, PA, USA (pp. 173–176).

    Google Scholar 

  39. UzZaman, N., & Allen, J. (2011). Temporal evaluation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 351–356).

    Google Scholar 

  40. Souza, J. D., & Ng, V. (2013). Classifying temporal relations with rich linguistic knowledge. In Proceedings of NAACL HLT (pp. 918–927).

    Google Scholar 

  41. Laokulrat, N., Miwa, M., Tsuruoka, Y., & Chikayama, T. (2013). Uttime: Temporal relation classification using deep syntactic features. In Proceedings of the Seventh International Workshop on Semantic Evaluation (pp. 88–92).

    Google Scholar 

  42. Wu, G., & Zhu, Z. (2014). An enhanced discriminability recurrent fuzzy neural network for temporal classification problems. Fuzzy Sets and Systems, 237, 47–62. https://doi.org/10.1016/j.fss.2013.05.007.

    Article  MathSciNet  MATH  Google Scholar 

  43. Campos, R., Dias, G., Jorge, A. M., & Nunes, C. (2014). C GTE-Cluster: A temporal search interface for implicit. In Proceedings of the European Conference on IR Research (pp. 775–779).

    Google Scholar 

  44. Uma, V., Nikhila, L., & Aghila, G. (2016). RaTeR. In Proceedings of the International Conference on Informatics and Analytics—ICIA-16. https://doi.org/10.1145/2980258.2980288.

  45. Xu, T., Mcnamee, P., & Oard, D. W. (2013). HLTCOE at TREC 2013: Temporal summarization. In Proceedings of the 22nd Text Retrieval Conference (TREC’13).

    Google Scholar 

  46. McCreadie, R., Macdonald, C., & Ounis, I. (2014). Incremental update summarization. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management—CIKM’14 (pp. 301–310). https://doi.org/10.1145/2661829.2661951.

  47. Wang, B., Liakata, M., Tsakalidis, A., Kolaitis, S. G., Papadopoulos, S., Apostolidis, L., et al. (2017). TOTEMSS: Topic-based, temporal sentiment summarisation for Twitter. In Proceedings of the IJCNLP, System Demonstrations (pp. 21–24).

    Google Scholar 

  48. McCreadie, R., Santos, R., Macdonald, C., & Ounis, I. (2018). Explicit Diversification of Event Aspects for Temporal Summarization. ACM Transactions on Information Systems, 36, 1–31. https://doi.org/10.1145/3158671.

    Article  Google Scholar 

  49. Radinsky, K., & Horvitz, E. (2013). Mining the web to predict future events. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining—WSDM’13 (pp. 255–264). https://doi.org/10.1145/2433396.2433431.

  50. Zhang, S., Bahrampour, S., Ramakrishnan, N., Schott, L., & Shah, M. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. In IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5970–5974).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE, UIET, Panjab University, Chandigarh, 160014, India

    Rakshita Bansal, Monika Rani, Harish Kumar & Sakshi Kaushal

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  1. Rakshita Bansal
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  2. Monika Rani
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  3. Harish Kumar
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  4. Sakshi Kaushal
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Corresponding author

Correspondence to Rakshita Bansal .

Editor information

Editors and Affiliations

  1. Central University of Karnataka, Kalaburagi, Karnataka, India

    N. R. Shetty

  2. National Institute of Advanced Studies, Bangalore, Karnataka, India

    L. M. Patnaik

  3. Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    H. C. Nagaraj

  4. Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    Prasad Naik Hamsavath

  5. Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    N. Nalini

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Bansal, R., Rani, M., Kumar, H., Kaushal, S. (2019). Temporal Information Retrieval and Its Application: A Survey. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_19

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  • DOI: https://doi.org/10.1007/978-981-13-6001-5_19

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