Abstract
Event relation identification is an important branch of event research. This paper proposes a new event relation identification method from the semantic point of view between events. Use the dependency between events and the co-occurrence of the event elements in the text, and then construct a set of semantic event clues. Then use the improved AP algorithm to cluster the event set with the related thread. Experimental results show that using the six semantic elements of events can more accurately calculate the dependency between candidate related events and the correlation between candidate related event elements, it could find more candidate related events and improve the recognition ability of event relations.
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
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)
Pekar, V.: Acquisition of verb entailment from text. In: Proceedings of the Human Language Technology Conference of the NAACL, New York, USA, pp. 49–56 (2006)
Lin, D., Pantel, P.: Discovery of inference rules from text. In: Proceeding of the 7th ACM SIGKDD, San Francisco, California, USA, pp. 323–328 (2001)
Mani, I., Wellner, B., Verhagen, M., et al.: Machine learning of temporal relations. In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 753–760 (2006)
Davidov, D., Rappoport, A., Koppel, M.: Fully unsupervised discovery of concept-specific relations by web mining. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. pp. 232–239 (2007)
Rosenfeld, B., Feldman, R.: Clustering for unsupervised relation identification. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management, pp. 411–418. ACM (2007)
Hashimoto, C., Torisawa, K., Kloetzer, J., et al.: Toward future scenario generation: extracting event causality exploiting semantic relation, context, and association features. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1. Association for Computational Linguistics (2014)
Ma, B., Hong, Y., Yang, X., et al.: Using event dependency cue inference to recognize event relation. Acta Sci. Natur. Univ. 49(1), 109–116 (2013)
Zhong, Z., Liu, Z., Zhou, W.: The model of event relation representation. J. Chinese Inf. Process. 23(6), 56–60 (2009)
Zhou, G.D., Zhang, M., Ji, D.H., et al.: Tree kernel-based relation extraction with context-sensitive structured parse tree information. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 728–736 (2007)
Liu, Z., Huang, M., Zhou, W., et al.: Research on event-oriented ontology model. Comput. Sci. 36(11), 189–192, 199 (2009)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Guan, R., Pei, Z., Shi, X., et al.: Weight affinity propagation and its application to text clustering. J. Comput. Res. Dev. 47(10), 1733–1740 (2010)
Ahmad, W., Narayanan, A.: Feature weighing for efficient clustering. In: 6th International Conference on Advanced Information Management and Service, Seoul, pp. 236–242 (2010)
Fu, J., Liu, W., Liu, Z.: A study of Chinese event taggability. In: Proceedings of the 2nd International Conference on Communication Software and Networks, Singapore, pp. 400–404 (2010)
Yang, X., Hong, Y., et al.: Event relation recognition by event term and entity inference. J. Chinese Inf. Process. 28(2), 100–108 (2014)
Kolya, A.K., Ekbal, A., Bandyopadhyay, S.: Event-event relation identification a CRF based approach. In: Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE, pp. 1–8 (2010)
Acknowledgements
This research was partially supported by the Natural Science Foundation of China (No. 61273328, 61305053) and Innovation Program of Shanghai Municipal Education Commission (No. 14YZ151).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, J., Liu, Z., Liu, W. (2019). Event Relation Identification Based on Dependency and Co-occurrence. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_26
Download citation
DOI: https://doi.org/10.1007/978-981-13-6473-0_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6472-3
Online ISBN: 978-981-13-6473-0
eBook Packages: Computer ScienceComputer Science (R0)