Abstract
Natural language text contains numerous event-based, and a large number of semantic relations exist between events. Event relations express the event rationality logic and reveal the evolution process of events, which is of great significance for machines to understand the text and the construction of event-based knowledge base. Event relation discovery includes extracting event relation from text and obtaining event relation by reasoning. Event relation extraction focuses on the recognition of explicit relations, while event relation reasoning can also discover implicit relations, which is more meaningful and more difficult. In this paper, we propose a model combining LSTM and attention mechanism for event relation reasoning, which uses the attention mechanism to dynamically generate event sequence representation according to the type of relation and predicts the event relation. The macro-F1 value in the experimental result reaches 63.71%, which shows that the model can effectively discover implicit event-event relation.
Supported by the National Key Research and Development Program of China (No. 2017YFE0117500), the National Natural Science Foundation of China (No. 61991410), the research project of the 54th Research Institute of China Electronics Technology Group (No. SKX192010019).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aldawsari, M., Finlayson, M.A.: Detecting subevents using discourse and narrative features. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)
Araki, J., Liu, Z., Hovy, E.H., Mitamura, T.: Detecting subevent structure for event coreference resolution. In: LREC, pp. 4553–4558 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS) pp. 1–9 (2013)
Cheng, F., Miyao, Y.: Classifying temporal relations by bidirectional LSTM over dependency paths. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 1–6 (2017)
Dai, Q., Kong, F., Dai, Q.: Event temporal relation classification based on graph convolutional networks. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 393–403. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32236-6_35
Girju, R., Moldovan, D.: Mining answers for causation questions. In: Proceedings of the AAAI Spring Symposium, October 2002
Glavaš, G., Šnajder, J.: Constructing coherent event hierarchies from news stories. In: Proceedings of TextGraphs-9: the Workshop on Graph-Based Methods for Natural Language Processing, pp. 34–38 (2014)
Kruengkrai, C., Torisawa, K., Hashimoto, C., Kloetzer, J., Oh, J.H., Tanaka, M.: Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI 2017, p. 3466–3473. AAAI Press (2017)
Liu, Z., Mitamura, T., Hovy, E.: Graph-based decoding for event sequencing and coreference resolution. arXiv preprint arXiv:1806.05099 (2018)
Liu, Z., Huang, M., Zhou, W., Zhong, Z., Fu, J., Shan, J., Zhi, H.: Research on event-oriented ontology model. Comput. Sci. 36(11), 189–192 (2009)
Mani, I., Verhagen, M., Wellner, B., Lee, C., Pustejovsky, J.: Machine learning of temporal relations. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 753–760 (2006)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mirza, P.: Extracting temporal and causal relations between events. In: Proceedings of the ACL 2014 Student Research Workshop, pp. 10–17 (2014)
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, pp. 308–317 (2014)
Nguyen, T., Grishman, R.: Graph convolutional networks with argument-aware pooling for event detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Ning, Q., Subramanian, S., Roth, D.: An improved neural baseline for temporal relation extraction. arXiv preprint arXiv:1909.00429 (2019)
Peng, H., Song, Y., Roth, D.: Event detection and co-reference with minimal supervision. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 392–402 (2016)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
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: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1785–1794 (2015)
Yang, Z., Liu, W., Liu, Z.: Event causality identification by modeling events and relation embedding. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11303, pp. 59–68. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04182-3_6
Zhang, Y., Li, P., Zhou, G.: Classifying temporal relations between events by deep bilstm. In: 2018 International Conference on Asian Language Processing (IALP), pp. 267–272. IEEE (2018)
Zhou, B., Ning, Q., Khashabi, D., Roth, D.: Temporal common sense acquisition with minimal supervision. arXiv preprint arXiv:2005.04304 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, T., Liu, W., Li, W., Wu, J., Ren, H. (2021). Event Relation Reasoning Based on Event Knowledge Graph. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-82136-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82135-7
Online ISBN: 978-3-030-82136-4
eBook Packages: Computer ScienceComputer Science (R0)