Advertisement

Extracting Temporal Event Relations Based on Event Networks

  • Duc-Thuan VoEmail author
  • Ebrahim Bagheri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Temporal event relations specify how different events expressed within the context of a textual passage relate to each other in terms of time sequence. There have already been impactful work in the area of temporal event relation extraction; however, they are mostly supervised methods that rely on sentence-level textual, syntactic and grammatical structure patterns to identify temporal relations. In this paper, we present an unsupervised method that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 corpus and compare our work with several strong baselines. We show that our unsupervised method is able to show better performance in terms of precision and f-measure over it supervised counterparts.

References

  1. 1.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefGoogle Scholar
  2. 2.
    Abacha, A.B., Zweigenbaum, P.: MEANS: a medical question-answering systems combining NLP techniques and semantic web technologies. Inf. Process. Manag. 51, 570–594 (2016)CrossRefGoogle Scholar
  3. 3.
    Chambers, N., Cassidy, T., McDowell, B., Bethard, S.: Dense event ordering with a multi-pass architecture. Trans. Assoc. Comput. Linguist. 2, 273–284 (2014)CrossRefGoogle Scholar
  4. 4.
    Corro, L.D., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of the 22nd international conference on World Wide Web (WWW 2013), Rio de Janeiro, Brazil, 13–17 May 2013, pp. 355–366 (2013)Google Scholar
  5. 5.
    Ji, H., Favre, B., Lin, W.P., Gillick, D., Hakkani-Tur, D., Grishman, R.: Open-domain multi-document summarization via information extraction: challenges and prospects. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds.) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Laokulrat, N., Miwa, M., Tsuruoka, Y.: Stacking approach to temporal relation classification with temporal inference. J. Nat. Lang. Process. 22(3), 171–196 (2015)CrossRefGoogle Scholar
  7. 7.
    Laokulrat, N., Miwa, M., Tsuruoka, Y., Chikayama, T.: Uttime: temporal relation classification using deep syntactic features. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (Se-mEval 2013), Atlanta, Georgia, USA, June, pp. 88–92. Association for Computational Linguistics (2013)Google Scholar
  8. 8.
    Mausam, Schmitz, M., Bart, R., Soderland, S.: Open language learning for information extraction. In: Proceedings of the 2012 conference on Empirical Methods in Natural Language Processing (EMNLP 2012), Jeju Island, Korea, 12–14 July 2012, pp. 523–534 (2012)Google Scholar
  9. 9.
    Mirza, P., Tonelli, S.: Classifying temporal relations with simple features. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, April, pp. 308–317. Association for Computational Linguistics (2014)Google Scholar
  10. 10.
    Mirza, P., Tonelli, S.: Catena: causal and temporal relation extraction from natural language texts. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 64–75. Association for Computational Linguistic (2016)Google Scholar
  11. 11.
    Souza, J.D., Ng, V.: Classifying temporal relations with rich linguistic knowledge. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, June, pp. 918–927. Association for Computational Linguistics (2013)Google Scholar
  12. 12.
    UzZaman, N., Lorens, H., Derczynski, L., Allen, J., Verhagen, M., Pustejovsky, J.: Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, Georgia, USA, June, pp. 1–9. Association for Computational Linguistics (2013)Google Scholar
  13. 13.
    Verhagen, M., Sauri, S., Caselli, T., Pustejovsky, J.: SemEval-2010 task 13: TempEval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, Stroudsburg, PA, USA, pp. 57–62. Association for Computational Linguistics (2010)Google Scholar
  14. 14.
    Vo, D.T., Bagheri, E.: Open information extraction. Encycl. Seman. Comput. Robot. Intell. 1(1) (2017).  https://doi.org/10.1142/s2425038416300032CrossRefGoogle Scholar
  15. 15.
    Vo, D.T., Bagheri, E.: Self-training on refined clause patterns for relation extraction. Inf. Process. Manag. 54, 686–706 (2018)CrossRefGoogle Scholar
  16. 16.
    Zhou, G., Qian, L., Fan, J.: Tree kernel based semantic relation extraction with rich syntactic and semantic information. Inf. Sci. 180, 1313–1325 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Mendes, P.N., Mühleisen, H., Bizer, C.: Sieve: linked data quality assessment and fusion. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops (EDBT-ICDT 2012), pp. 116–123 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory for Systems, Software and Semantics (LS3)Ryerson UniversityTorontoCanada

Personalised recommendations