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Open AccessArticle

Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning

1
Department of Geography, National Taiwan University, Taipei 10617, Taiwan
2
Department of Economics, National Taiwan University, Taipei 10617, Taiwan
3
School of Big Data Management, Soochow University, Taipei 111002, Taiwan
4
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4234; https://doi.org/10.3390/app10124234
Received: 28 May 2020 / Revised: 12 June 2020 / Accepted: 15 June 2020 / Published: 20 June 2020
This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management. The hybrid NER approach proposed in this research includes three modules: (1) data augmentation, which constructs a concise data set for disaster management; (2) reference model, which utilizes the bidirectional long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets. Through the combination of established rules and learned sentence patterns, the hybrid approach performs well in NER tasks for disaster management and recognizes unfamiliar words successfully. This research applied the proposed NER module to disaster management. In the application, we favorably handled the NER tasks of our related work and achieved our desired outcomes. Through proper transfer, the results of this work can be extended to other fields and consequently bring valuable advantages in diverse applications. View Full-Text
Keywords: damage information gathering; disaster management; data augmentation; transfer learning; named entity recognition; chatbot damage information gathering; disaster management; data augmentation; transfer learning; named entity recognition; chatbot
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MDPI and ACS Style

Kung, H.-K.; Hsieh, C.-M.; Ho, C.-Y.; Tsai, Y.-C.; Chan, H.-Y.; Tsai, M.-H. Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning. Appl. Sci. 2020, 10, 4234. https://doi.org/10.3390/app10124234

AMA Style

Kung H-K, Hsieh C-M, Ho C-Y, Tsai Y-C, Chan H-Y, Tsai M-H. Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning. Applied Sciences. 2020; 10(12):4234. https://doi.org/10.3390/app10124234

Chicago/Turabian Style

Kung, Hung-Kai; Hsieh, Chun-Mo; Ho, Cheng-Yu; Tsai, Yun-Cheng; Chan, Hao-Yung; Tsai, Meng-Han. 2020. "Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning" Appl. Sci. 10, no. 12: 4234. https://doi.org/10.3390/app10124234

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