EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings

Article

Shi, Kaize, Wang, Yusen, Lu, Hao, Zhu, Yifan and Niu, Zhendong. 2021. "EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings." Information Processing and Management. 58 (4). https://doi.org/10.1016/j.ipm.2021.102564
Article Title

EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings

ERA Journal ID17904
Article CategoryArticle
AuthorsShi, Kaize, Wang, Yusen, Lu, Hao, Zhu, Yifan and Niu, Zhendong
Journal TitleInformation Processing and Management
Journal Citation58 (4)
Article Number102564
Number of Pages23
Year2021
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0306-4573
1873-5371
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ipm.2021.102564
Web Address (URL)https://www.sciencedirect.com/science/article/abs/pii/S0306457321000637
Abstract

With the frequent occurrence of extreme natural phenomena, news about meteorological disasters has increased. As a timely and effective social sensor, social networks have gradually become an important data source for the perception of extreme meteorological events. Meteorological briefing refers to screening valuable knowledge from massive data to provide decision-makers with efficient situational awareness support. However, social network-based briefing content has challenges, including colloquialisms and informal text styles. How to optimize these data in a formal text style is of great significance to improve decision-making efficiency. This paper proposes a meteorological briefing formalization module composed of three models: the text form judgment model, the formalization words detection model, and the event knowledge guided text formalization (EKGTF) model. These models are concatenated to optimize the meteorological briefing, specifically formalizing the briefing content’s language style based on Sina Weibo data. As a knowledge-enhanced model, the EKGTF model focuses on describing the core meteorological event knowledge while formalizing the content. Compared to baseline models, the EKGTF model achieves the best results on the BLEU score. Based on the meteorological briefing formalization module, a meteorological briefing formalization service framework is constructed, which is to be applied to the China Meteorological Administration (CMA) Public Meteorological Service Center.

KeywordsEvent knowledge guided text formalization model; Fine-tuned BERT model; Meteorological event knowledge; Meteorological briefing formalization service framework; Meteorological decision support platform
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
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Byline AffiliationsBeijing Institute of Technology, China
Chinese Academy of Sciences, China
University of Pittsburgh, United States
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