Author
Listed:
- Danjie Chen
(College of Environment and Planning, Henan University, Kaifeng 475004, China
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China)
- Fen Qin
(College of Environment and Planning, Henan University, Kaifeng 475004, China
Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China)
- Kun Cai
(College of Environment and Planning, Henan University, Kaifeng 475004, China
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China)
- Yatian Shen
(College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China)
Abstract
Typhoons are major natural disasters in China. Much typhoon information is contained in a large number of network media resources, such as news reports and volunteered geographic information (VGI) data, and these are the implicit data sources for typhoon research. However, two problems arise when using typhoon information from Chinese news reports. Since the Chinese language lacks natural delimiters, word segmentation error results in trigger mismatches. Additionally, the polysemy of Chinese affects the classification of triggers. Second, there is no authoritative classification system for typhoon events. This paper defines a classification system for typhoon events, and then uses the system in a neural network model, lattice-structured bidirectional long–short-term memory with a conditional random field (BiLSTM-CRF), to detect these events in Chinese online news. A typhoon dataset is created using texts from the China Weather Typhoon Network. Three other datasets are generated from general Chinese web pages. Experiments on these four datasets show that the model can tackle the problems mentioned above and accurately detect typhoon events in Chinese news reports.
Suggested Citation
Danjie Chen & Fen Qin & Kun Cai & Yatian Shen, 2021.
"Detecting and Classifying Typhoon Information from Chinese News Based on a Neural Network Model,"
Sustainability, MDPI, vol. 13(13), pages 1-20, June.
Handle:
RePEc:gam:jsusta:v:13:y:2021:i:13:p:7332-:d:585756
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