Pattern Based Extraction of Times from Natural Language Text

  • Vanitha GudaEmail author
  • Suresh Kumar Sanampudi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 8)


Nowadays data sources are producing huge amount of data everywhere namely news data, wikis, web crawlers and many other databases, data needs to be analyze and exploited to obtain the required information to build several knowledge databases. In this context time is an essential component of information space and it is a real phenomenon which makes a continuous change through which we live. The information which is associated with time is termed as temporal information and the process of identifying and retrieving times are called as temporal information extraction (TIE). The temporal information extraction is useful in many natural language processing (NLP) applications like information extraction for generating better text summaries and temporal question answering (Q.A) systems for time related searches, and information retrievals etc. Times are also useful to order the events in the text, the ordering can be from the past through the present into the future and this will helpful to measure the durations of an event occurrence and find outs the relation between them. In this paper we present our work for extraction of various forms of times from natural language by using pattern rules. The results obtained from the experiments found to have better precision value when compared with existing methods.


Times extraction Temporal information extraction Natural language processing 


  1. 1.
    Roser Sauri, Robert Knippen, Marc Verhagen, and James Pustejovsky. Evita: a robust event recognizer for QA systems. In HLT ’05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 700–707, Morristown, NJ, USA, 2005.Google Scholar
  2. 2.
    SunghwanSohn, Kavishwar B Wagholikar, Dingcheng Li, Siddhartha R Jonnalagadda, Cui Tao, Ravikumar Komandur Elayavilli, and Hongfang Liu. Comprehensive temporal information detection from clinical text: medical events, time, and tlink identification. Journal of the American Medical Informatics Association, pages amiajnl-2013.Google Scholar
  3. 3.
    Yan Xu, Yining Wang, Tianren Liu, Junichi Tsujii, I Eric, and Chao Chang. An end-to-end system to identify temporal relation in discharge summaries: 2012.Google Scholar
  4. 4.
    Schilder, F., and Habel, C. From temporal expressions to temporal information: Semantic tagging of news messages. In Proceedings of the ACL-2001.Google Scholar
  5. 5.
    Verhagen, M.; Mani, I.; Saur´ı, R.; Knippen, R.; Littman, J.; and Pustejovsky, J. Automating Temporal Annotation with TARSQI-2005.Google Scholar
  6. 6.
    Allen, J. Maintaining knowledge about temporal intervals. C. ACM 26(11):832–843, 1983.Google Scholar
  7. 7.
    Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 2006.Google Scholar
  8. 8.
  9. 9.
    Vanitha Guda, Suresh Kumar Sanampudi.: Rule based Event Extraction in Natural Language Text, In Proceedings of IEEE international conference of Recent Trends in ISBN 978-1-5090-0773-5/16/$31.00 © 2016 IEEE, pp. 47–51, May 2016. Google Scholar
  10. 10.
    N. Chambers, S. Wang, and D. Jurafsky. Classifying temporal relations between events. Proceedings of the ACL 2007.Google Scholar
  11. 11.
    B. Boguraev and R. K. Ando. Time Bank-Driven TimeML analysis. In Annotating, Extracting and Reasoning about Time and Events, Dagstuhl Seminar Proceedings. Dagstuhl, Germany, 2005.Google Scholar
  12. 12.
    Pustejovsky, J., Castano, J., Ingria, R. Saur´ı, R., Gaizauskas, R., Setzer, A., Katz, G., and Radev, D., TimeML: Robust specification of event and temporal expressions in text. In IWCS-5 Fifth Intl. Workshop on Computational Semantics-2003.Google Scholar
  13. 13.
    Angel X Chang and Christopher D Manning. Sutime: A library for recognizing and normalizing time expressions. In LREC, pages 3735–3740, 2012.Google Scholar
  14. 14.
  15. 15.
    Verhagen, M.; Mani, I.; Saur´ı, R.; Knippen, R.; Littman, J.; and Pustejovsky, J., Automating Temporal Annotation with TARSQI. In Demo Session. Proceedings of ACL, 2005.Google Scholar
  16. 16.
    Bramsen, P.; Deshpande, P.; Lee, Y.; and Barzilay, R. Inducing temporal graphs. In Procs. of EMNLP, 189–198, 2006.Google Scholar
  17. 17.
    Mani, I.; Schiffman, B.; and Zhang, J. Inferring temporal ordering of events in news. In Procs. of HLT-NAACL, 2003.Google Scholar
  18. 18.
    JannikStrotgen and Michael Gertz. Heideltime: High quality rule-based extraction and normalization of temporal expressions. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 321–324. Association for Computational Linguistics, 2010.Google Scholar
  19. 19.
    Daniel M. Bikel, Richard Schwartz, and Ralph M. Weischedel. An algorithm that learns what is I in a name. Mach. Learn., 34(1–3):211–231, ISSN0885-6125.  10.1023/A:1007558221122, feb 1999.
  20. 20.
    J. Poveda, M. Surdeanu and J. Turmo. A Comparison of Statistical and Rule-Induction Learners for Automatic Tagging of Time Expressions in English. In Proceedings of the International Symposium on Temporal Representation and Reasoning, 2007.Google Scholar
  21. 21.
    Corinna Cortes and Vladimir Vapnik. Support-vector networks. In Machine Learning, pages 273–297, 1995.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.JNTUH, Chaitanya Bharathi Institute of TechnologyHyderabadIndia
  2. 2.JNTUHCEJKarimnagarIndia

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