ITM Web Conf.
Volume 45, 20222021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|Number of page(s)||9|
|Section||Computer Technology and System Design|
|Published online||19 May 2022|
Research on targeted service technology for early warning information of meteorological disaster based on NLP
CMA Public Meteorological Service Center, 100081 Beijing, China
2 Meteorological Service Center in Heilongjiang Province, 150030 Heilongjiang Province, China
* Corresponding author: firstname.lastname@example.org
Affected by global climate change and the superposition of urban construction, the climate of city meteorological guarantee service puts forward new requirements and new challenges. As the first line of defense of meteorological disaster prevention and reduction, it is very important to realize the refinement targeted service of early warning information. There are some problems in the traditional early warning information service, such as the difficulty of accurate service of early warning information, the lack of precision, and the insufficient mining of early warning text information. This paper mainly analyzes the text description characteristics of early warning information of meteorological disaster, constructs the early warning information knowledge extraction process, constructs the early warning information labeling system, and realizes the early warning effective time extraction method based on conditional random field model, Early warning affected areas extraction method based on bidirectional long-term and short-term memory neural network and early warning cautions extraction method based on bootstrapping weak supervised learning method. Finally, taking the early warning information targeting service of meteorological information decision support system as an example, this paper tests the early warning information extraction methods, and preliminarily realizes the early warning precision targeting service in the decision support service of meteorological disaster prevention and reduction.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.