@inproceedings{6c21fc4be09c48089f46173d303ba5b2,
title = "EEG: Knowledge base for event evolutionary principles and patterns",
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.",
keywords = "Event evolutionary graph, Knowledge base, Sequential relation between events, Social media",
author = "Zhongyang Li and Sendong Zhao and Xiao Ding and Ting Liu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 6th National Conference on Social Media Processing, SMP 2017 ; Conference date: 14-09-2017 Through 17-09-2017",
year = "2017",
doi = "10.1007/978-981-10-6805-8\_4",
language = "英语",
isbn = "9789811068041",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "40--52",
editor = "Huan Liu and Xing Xie and Xueqi Cheng and Huawei Shen and Weiying Ma and Shizheng Feng",
booktitle = "Social Media Processing - 6th National Conference, SMP 2017, Proceedings",
address = "德国",
}