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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2024

10.06.2023 | Original Article

Semantic rule-based information extraction for meteorological reports

verfasst von: Mengmeng Cui, Ruibin Huang, Zhichen Hu, Fan Xia, Xiaolong Xu, Lianyong Qi

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2024

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Abstract

Meteorological reports are one of the most important means of recording the weather conditions of a place over a period of time, and the existence of a large number of meteorological reports creates a huge demand for text processing and information extraction. However, valuable data and information are still buried deep in the mountain of meteorological reports, and there is an urgent need for an automated information extraction technique to help people integrate data from multiple meteorological reports and perform data analysis for a more comprehensive understanding of a specific meteorological topic or domain. Named entity recognition (NER) technique can extract useful entity information from meteorological reports. By analyzing the characteristics of nested entities in meteorological reports, this paper further proposes to introduce Multi-Conditional Random Fields (Multi-CRF), which uses each layer of CRF to output the recognition results of each type of entities, which helps to solve the problem of identifying nested entities in meteorological reports. The experimental results show that our model achieves state-of-the-art results. The final recognition results provide effective data support for automatic text verification recognition in the meteorological domain and provide important practical value for the construction of knowledge graphs of related meteorological reports.
Vorheriger Artikel Integrated GCN-LSTM stock prices movement prediction based on knowledge-incorporated graphs construction
Nächster Artikel Dimension-divided feature smoothing for graph neural network

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Metadaten
Titel
Semantic rule-based information extraction for meteorological reports
verfasst von
Mengmeng Cui
Ruibin Huang
Zhichen Hu
Fan Xia
Xiaolong Xu
Lianyong Qi
Publikationsdatum
10.06.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01885-8

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