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Sample of storm surge disaster cases.

Sample of storm surge disaster cases.

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Figure 1. Decomposition of events in the spatial dimension in storm...
Figure 2. Sample of storm surge disaster cases.
Figure 3. Architecture of the similarity model between natural disaster...
Figure 4. The concept of storm surge disaster domain ontology.
+10 Figure 5. Fuzzy interval affiliation function.
Similarity Calculation of Sudden Natural Disaster Cases with Fused Case Hierarchy—Taking Storm Surge Disasters as Examples
Article
Full-text available
  • Aug 2022
Sudden natural disasters have the characteristics of complexity, unpredictability and frequency. To better manage and analyze sudden natural disasters promptly with the help of historical natural disaster cases, this study adopts the method of fused case structure to calculate the similarity between sudden disaster cases. Based on the disaster info...
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... defined a case as a collection of information, which combined the disaster information suffered by a disasterbearing body and the disaster-generating environment in a sudden natural disaster. A sample of sudden natural disasters from storm surges is shown in Figure 2. ...

Citations

Joint entity–relation extraction for natural disaster based on table filling
Article
  • Jan 2023
  • J INF SCI
In this article, we propose a joint extraction of entity–relation triplets in natural disaster cases based on word pair relation table filling. For computational accuracy concerns or other reasons, traditional works often do entity recognition and relation extraction separately; it might put less attention over the task connections and triplet global association. We propose the Global Table Attention GRU (GL-TGRU) model as a joint approach that uses sequence information encoding and table information encoding to jointly learn the representation and enhance the global association of entity and relation in table filling. We evaluated the proposed model on the public data set SciERC and the natural disaster data set SSD-HDS, respectively. The F 1 scores of experimental results of the GL-TGRU model for entity identification and relation extraction achieved 65.81% and 37.30% on SciERC and achieved 93.89% and 84.06% on SSD-HDS. The results show our model helps to capture more global association relations of entity and relation, which can better identify the entity–relation triplet information.
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