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International Conference on Document Analysis and Recognition

ICDAR 2021: Document Analysis and Recognition – ICDAR 2021 pp 762–777Cite as

Temporal Ordering of Events via Deep Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12822))

Abstract

Ordering events with temporal relations in texts remains a challenge in natural language processing. In this paper, we introduce a new combined neural network architecture that is capable of classifying temporal relations between events in an Arabic sentence. Our model consists of two branches: the first one extracts the syntactic information and identifies the orientation of the relation between the two given events based on a Shortest Dependency Path (SDP) layer with Long and Short Memory (LSTM), and the second one encourages the model to focus on the important local information when learning sentence representations based on a Bidirectional-LSTM (BiLSTM) attention layer. The experiments suggest that our proposed model outperforms several previous state-of-the-art methods, with an F1-score equal to 86.40%.

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Notes

  1. 1.

    Temporal relations: After, Before, I-before, I-after, Begins, begun-by, Ends, Ends-by, During, During-INV, Includes, IS-Included, Simultaneous, Overlaps, overlapped by, Identity.

  2. 2.

    udpipe tool: http://lindat.mff.cuni.cz/services/udpipe/.

  3. 3.

    Similar events list:( , Perished), ( , Perished), ( , killed), ( , burned), ( , martyred), ( , died), ( , Perished), ( , died), ( , died), ( , died), ( , died), ( , Perished).

  4. 4.

    The enriched corpus can be found at: https://github.com/nafaa5/Enriched-Arabic-TimeML-Corpus.

References

  1. Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26, 832–843 (1983)

    Article  Google Scholar 

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)

    Google Scholar 

  3. Bethard, S.: Cleartk-timeml: a minimalist approach to tempeval 2013. In: SemEval@NAACL-HLT, vol. 2, pp. 10–14 (2013)

    Google Scholar 

  4. Bsir, B., Zrigui, M.: Document model with attention bidirectional recurrent network for gender identification. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11506, pp. 621–631. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20521-8_51

    Chapter  Google Scholar 

  5. Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: HLT/EMNLP, pp. 724–731 (2005)

    Google Scholar 

  6. Chambers, N.: Navytime: event and time ordering from raw text. In: SemEval@NAACL-HLT, vol. 2, pp. 73–77 (2013)

    Google Scholar 

  7. Chambers, N., Cassidy, T., McDowell, B., Bethard, S.: Dense event ordering with a multi-pass architecture. Trans. ACL 2, 273–284 (2014)

    Google Scholar 

  8. Chambers, N., Wang, S., Jurafsky, D.: Classifying temporal relations between events. In: ACL (2007)

    Google Scholar 

  9. Dligach, D., Miller, T., Lin, C., Bethard, S., Savova, G.: Neural temporal relation extraction. In: EACL, vol. 2 (2017)

    Google Scholar 

  10. Do, H.W., Jeong, Y.S.: Temporal relation classification with deep neural network. In: BigComp, pp. 454–457 (2016)

    Google Scholar 

  11. D’Souza, J., Ng, V.: Classifying temporal relations with rich linguistic knowledge. In: HLT-NAACL (2013)

    Google Scholar 

  12. Glavas, G., Šnajder, J.: Construction and evaluation of event graphs. Nat. Lang. Eng. 21(4), 607–652 (2015)

    Article  Google Scholar 

  13. Haffar, N., Hkiri, E., Zrigui, M.: Arabic linguistic resource and specifications for event annotation. In: IBIMA, pp. 4316–4327 (2019)

    Google Scholar 

  14. Haffar, N., Hkiri, E., Zrigui, M.: TimeML annotation of events and temporal expressions in Arabic texts. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11683, pp. 207–218. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28377-3_17

    Chapter  Google Scholar 

  15. Haffar, N., Hkiri, E., Zrigui, M.: Enrichment of Arabic TimeML corpus. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 655–667. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_51

    Chapter  Google Scholar 

  16. Haffar, N., Hkiri, E., Zrigui, M.: Using bidirectional LSTM and shortest dependency path for classifying Arabic temporal relations. KES. Procedia Comput. Sci. 176, 370–379 (2020)

    Article  Google Scholar 

  17. Hkiri, E., Mallat, S., Zrigui, M.: Events automatic extraction from Arabic texts. Int. J. Inf. Retr. Res. 6(1), 36–51 (2016)

    Google Scholar 

  18. Hkiri, E., Mallat, S., Zrigui, M.: Integrating bilingual named entities lexicon with conditional random fields model for Arabic named entities recognition. In: ICDAR, pp. 609–614. IEEE (2017)

    Google Scholar 

  19. Hkiri, E., Mallat, S., Zrigui, M., Mars, M.: Constructing a lexicon of Arabic-English named entity using SMT and semantic linked data. Int. Arab J. Inf. Technol. 14(6), 820–825 (2017)

    Google Scholar 

  20. Kang, Y., Wei, H., Zhang, H., Gao, G.: Woodblock-printing Mongolian words recognition by bi-LSTM with attention mechanism. In: The International Conference on Document Analysis and Recognition (ICDAR), pp. 910–915 (2019)

    Google Scholar 

  21. Kolya, A.K., Kundu, A., Gupta, R., Ekbal, A., Bandyopadhyay, S.: JU\(\_\)CSE: a CRF based approach to annotation of temporal expression, event and temporal relations. In: SemEval@NAACL-HLT, vol. 2, pp. 64–72 (2013)

    Google Scholar 

  22. Laokulrat, N., Miwa, M., Tsuruoka, Y., Chikayama, T.: Uttime: temporal relation classification using deep syntactic features. In: SemEval@NAACL-HLT, vol. 2, pp. 88–92 (2013)

    Google Scholar 

  23. Li, Z., Cai, J., He, S., Zhao, H.: Seq2seq dependency parsing. In: The 27th International Conference on Computational Linguistics, pp. 3203–3214 (2018)

    Google Scholar 

  24. Lim, C.G., Choi, H.J.: LSTM-based model for extracting temporal relations from Korean text. In: BigComp, pp. 666–668 (2018)

    Google Scholar 

  25. Mahmoud, A., Zrigui, M.: Sentence embedding and convolutional neural network for semantic textual similarity detection in Arabic language. Arab. J. Sci. Eng. 44(11), 9263–9274 (2019)

    Article  Google Scholar 

  26. Mahmoud, A., Zrigui, M.: BLSTM-API: Bi-LSTM recurrent neural network-based approach for Arabic paraphrase identification. Arab. J. Sci. Eng. 46(4), 4163–4174 (2021)

    Article  Google Scholar 

  27. Mani, I., Verhagen, M., Wellner, B., Lee, C.M., Pustejovsky, J.: Machine learning of temporal relations. In: ACL (2006)

    Google Scholar 

  28. Meng, Y., Rumshisky, A., Romanov, A.: Temporal information extraction for question answering using syntactic dependencies in an LSTM-based architecture. In: EMNLP (2017)

    Google Scholar 

  29. Mirza, P., Tonelli, S.: Classifying temporal relations with simple features. In: EACL, pp. 308–317 (2014)

    Google Scholar 

  30. Pandit, O.A., Denis, P., Ralaivola, L.: Learning rich event representations and interactions for temporal relation classification. In: ESANN (2019)

    Google Scholar 

  31. Plank, B., Moschitti, A.: Embedding semantic similarity in tree kernels for domain adaptation of relation extraction. In: ACL, vol. 1, pp. 1498–1507 (2013)

    Google Scholar 

  32. Shen, Y., Huang, X.: Attention-based convolutional neural network for semantic relation extraction. In: COLING, pp. 2526–2536 (2016)

    Google Scholar 

  33. Soliman, A.B., Eissa, K., El-Beltagy, S.R.: Aravec: a set of Arabic word embedding models for use in Arabic NLP. In: ACLING, vol. 117, pp. 256–265 (2017)

    Google Scholar 

  34. Tourille, J., Ferret, O., Névéol, A., Tannier, X.: Neural architecture for temporal relation extraction: a Bi-LSTM approach for detecting narrative containers. In: ACL, vol. 2, pp. 224–230 (2017)

    Google Scholar 

  35. Tran, V.H., Phi, V.T., Shindo, H., Matsumoto, Y.: Relation classification using segment-level attention-based CNN and dependency-based RNN. In: NAACL-HLT, vol. 1, pp. 2793–2798 (2019)

    Google Scholar 

  36. UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., Pustejovsky, J.: SemEval-2013 task 1: TempEval-3: evaluating time expressions, events, and temporal relations. In: (SemEval-2013), vol. 1 (2013)

    Google Scholar 

  37. Wang, L., Cao, Z., de Melo, G., Liu, Z.: Relation classification via multi-level attention CNNs. In: ACL, vol. 1, pp. 1298–1307 (2016)

    Google Scholar 

  38. Xiao, M., Liu, C.: Semantic relation classification via hierarchical recurrent neural network with attention. In: COLING, pp. 1254–1263 (2016)

    Google Scholar 

  39. Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: EMNLP, pp. 1785–1794 (2015)

    Google Scholar 

  40. Zhang, X., Chen, F., Huang, R.: A combination of RNN and CNN for attention-based relation classification. Procedia Comput. Sci. 131, 911–917 (2018)

    Article  Google Scholar 

  41. Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: ACL, vol. 2, pp. 207–212 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ISITCom, University of Sousse, 4011, Hammam Sousse, Tunisia

    Nafaa Haffar

  2. Research Laboratory in Algebra, Numbers Theory and Intelligent Systems, University of Monastir, Monastir, Tunisia

    Nafaa Haffar, Emna Hkiri & Mounir Zrigui

  3. Computer Science Department, College of Science and Arts in Al Qurayyat, Jouf University, Sakakah, Kingdom of Saudi Arabia

    Rami Ayadi

  4. Higher Institute of Computer of Medenine, University of Gabes, Gabès, Tunisia

    Rami Ayadi

Authors
  1. Nafaa Haffar
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  2. Rami Ayadi
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  3. Emna Hkiri
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  4. Mounir Zrigui
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Corresponding author

Correspondence to Nafaa Haffar .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Haffar, N., Ayadi, R., Hkiri, E., Zrigui, M. (2021). Temporal Ordering of Events via Deep Neural Networks. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_49

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  • DOI: https://doi.org/10.1007/978-3-030-86331-9_49

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