Abstract
The evolution and development of events has its underlying principles, leading to events happened sequentially. Therefore, the discovery of such evolutionary patterns between events are of great value for event prediction, decision-making and scenario design of dialog system. In this paper, we propose Event Evolutionary Graph (EEG), which reveals evolutionary patterns and development logics between events. Specifically, we propose to construct EEG by recognizing the sequential relation between events and the direction of each sequential relation. For sequential relation and direction recognition, we explore the effectiveness of 4 categories of features: count-based, ratio-based, context-based and association-based features for correctly identifying sequential relations and corresponding directions. Experimental results show that (1) the framework we proposed is promising for EEG construction and (2) methods we proposed are effective for both sequential relation and direction recognition.
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Acknowledgments
This work was supported by the National Key Basic Research Program of China via grant 2014CB340503 and the National Natural Science Foundation of China (NSFC) via grants 61472107 and 61632011. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.
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Li, Z., Zhao, S., Ding, X., Liu, T. (2017). EEG: Knowledge Base for Event Evolutionary Principles and Patterns. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_4
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DOI: https://doi.org/10.1007/978-981-10-6805-8_4
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