News event prediction by trigger evolution graph and event segment

doi:10.23919/JSEE.2023.000083

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 615-626.doi: 10.23919/JSEE.2023.000083

• • 上一篇

收稿日期:2022-08-31 出版日期:2023-06-15 发布日期:2023-06-30

News event prediction by trigger evolution graph and event segment

Yaru ZHANG¹^,²(), Xijin TANG¹^,²^,*()

¹ Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
² University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-08-31 Online:2023-06-15 Published:2023-06-30
Contact: Xijin TANG E-mail:zhangyaru@amss.ac.cn;xjtang@iss.ac.cn
About author:
ZHANG Yaru was born in 1994. She is currently working towards her Ph.D. degree in Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Her research interests include natural language processing, social network analysis and knowledge management. E-mail: zhangyaru@amss.ac.cn

TANG Xijin was born in 1967. She is a full professor in Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). She received her B.E. degree in computer science and engineering from Zhejiang University, M.E. degree in management science and engineering from University of Science and Technology of China and Ph.D. degree from Institute of Systems Science, CAS. During her early system research and practice, she developed several decision support systems for water resources management, weapon system evaluation, e-commerce evaluation, etc. Now she is the secretary general of Systems Engineering Society of China. She also serves as vice president and secretary general of International Society for Knowledge and Systems Sciences. Her recent interests are meta-synthesis and advanced modeling, social network analysis and knowledge management, opinion mining and opinion dynamics, opinion big data and societal risk perception. E-mail: xjtang@iss.ac.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (71731002; 71971190)

摘要/Abstract

Abstract:

Event prediction aims to predict the most possible following event given a chain of closely related context events. Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information. Current datasets for event prediction, naturally, can be used for supervised learning. Event chains are either from document-level procedural action flow, or from news sequences under the same column. This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus, and adopts the standard multiple choice narrative cloze task evaluation. The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck. Based on trigger-guided structural relations in the event chains, we construct trigger evolution graph, and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy. Then there are features of two levels for each event, namely, text level semantic feature and trigger level structural feature. We design the attention mechanism to learn the features of event segments derived in term of event major subjects, and integrate relevance between event segments and the candidate event. The most possible next event is picked by the relevance. Experimental results on the real-world news corpus verify the effectiveness of the proposed model.

Key words: event prediction, trigger evolution graph, event segment

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 615-626.

Yaru ZHANG, Xijin TANG. News event prediction by trigger evolution graph and event segment[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 615-626.

图/表 6

参考文献 41

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Name	Number
News item	37 160
$ n $	9
$ k $	5
Event chain	3 281
Node	2 721
Edge	21 298

Model	Accuracy
Random	20.00
GGNN model	48.62
PairLSTM	49.23
Event pair based model+Attention_1	50.77
Event pair based model+Attention	50.77
Event pair based model+Attention+trigger representation	52.92
Event segment based model	54.77
Event segment based model+trigger representation	55.38
Event segment based model+trigger representation with general update way	52.62
Event segment based model+trigger representation-weighted loss	54.46

Model	Accuracy
PMI*	30.52
Event-Comp*	49.57
PairLSTM*	50.83
PairLSTM+trigger representation with general update way	51.13
PairLSTM+trigger representation	51.52

News event prediction by trigger evolution graph and event segment

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