Detecting and Classifying Typhoon Information from Chinese News Based on a Neural Network Model

Danjie Chen, Fen Qin, Kun Cai and Yatian Shen
Additional contact information
Danjie Chen: College of Environment and Planning, Henan University, Kaifeng 475004, China
Fen Qin: College of Environment and Planning, Henan University, Kaifeng 475004, China
Kun Cai: College of Environment and Planning, Henan University, Kaifeng 475004, China
Yatian Shen: College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

Sustainability, 2021, vol. 13, issue 13, 1-20

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.

Keywords: typhoon; classification; event detection; polysemy; lattice; BiLSTM-CRF; Chinese news reports (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/13/7332/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/13/7332/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:13:p:7332-:d:585756

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().