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

机译：EKGTF：一种知识增强模型，用于优化社会网络的气象简报

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摘要

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.
机译：随着极端自然现象的频繁发生，关于气象灾害的新闻已经增加。作为及时而有效的社交传感器，社交网络逐渐成为对极端气象事件感知的重要数据源。气象简报是指从大规模数据筛查有价值的知识，为具有有效的情境感知支持提供决策者。但是，基于社交网络的简报内容具有挑战，包括口语主义和非正式文本风格。如何以正式的文本方式优化这些数据具有重要意义，以提高决策效率。本文提出了一种由三种型号组成的气象简报形式化模块：文本形式判断模型，形式化词检测模型以及事件知识引导文本形式（EKGTF）模型。这些模型被连接以优化气象简报，特别是根据新浪微博数据正式形成简介内容的语言风格。作为一个知识增强的模型，EKGTF模型侧重于描述核心气象事件知识，同时正式化内容。与基线模型相比，EKGTF模型实现了BLEU评分的最佳结果。基于气象简报的正式化模块，构建了气象简报的形式化服务框架，该框架将适用于中国气象管理（CMA）公共气象服务中心。

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