Abstract:
Objectives Disaster event evolutionary graph can comprehensively express the development process of disaster, information of sub-events and multiple event relationships, providing knowledge services for disaster analysis. The problems in the construction of disaster event evolutionary graph include the lack of Chinese corpus containing spatiotemporal relationship, imprecise event extraction due to fuzzy boundaries of Chinese vocabulary, and difficulty in recognizing implied event relationships.
Methods This paper proposes a method for constructing disaster event evolutionary graph based on spatiotemporal relationship. First, a vocabulary enhancement event extraction model based on naming regularity (VENR) is designed to discern entity name patterns, and determine event information boundaries and types. Second, a relation recognition model by fusing attention and gated-dilated convolution (AGDC) is proposed to obtain multi-dimensional text features, thereby uncovering potential event relationships. Additionally, a Chinese disaster corpus with spatiotemporal relationship (CDCSTR) is established.
Results We experiment on CDCSTR and Chinese emergency corpus (CEC), the results show that the F1 scores of VENR are 88.59% and 78.49%,respectively on CDCSTR and CEC. Compared with the existing methods, AGDC improves the performance of four tasks on CDCSTR, especially excelling in spatial relationship recognition, achieving a performance lead of over 3.08%.
Conclusions The Chengdu-Leshan expressway tailgating accident event evolutionary graph is built for verification, enabling the effective expression of event evolution and spatiotemporal features in real disaster scene, consequently supporting emergency responses to disaster.
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