Citation

Shidik, Guruh Fajar and Winarsih, Nurul Anisa Sri and Saraswati, Galuh Wilujeng and Saputra, Filmada Ocky and Kusuma, Edi Jaya and Noersasongko, Edi and Goh, Gerald Guan Gan (2026) A hybrid systematic literature review and automated content analysis for named entity recognition in disaster information management. Natural Language Processing Journal, 15. p. 100207. ISSN 2949-7191

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Abstract

This study adopts a hybrid approach combining Systematic Literature Review (SLR) and Automated Content Analysis (ACA) to explore the application of Named Entity Recognition (NER) in disaster contexts. The growing demand for real-time, accurate information during disasters underscores the critical role of advanced information extraction techniques in enabling rapid decision-making and emergency response. This review investigates research trends, methodologies, and challenges in NER applications tailored for disaster management. Traditional rule-based NER methods often struggle with adaptability, whereas machine learning and deep learning approaches offer improved flexibility and scalability. Nonetheless, applying NER in disaster scenarios presents significant challenges, including handling complex linguistic variations, limited availability of annotated datasets, and processing diverse, unstructured text sources. By synthesizing current research, this study identifies key datasets, preprocessing methods, feature extraction techniques, and evaluation metrics that shape the development of NER models in disaster management. The findings aim to provide actionable insights into existing gaps and future directions, fostering the advancement of more robust and effective NER solutions to enhance disaster response and management worldwide.

Item Type:	Article
Uncontrolled Keywords:	Disaster management
Subjects:	H Social Sciences > HC Economic History and Conditions > HC92 Economic geography of the oceans (General)
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 May 2026 01:14
Last Modified:	07 May 2026 06:20
URII:	http://shdl.mmu.edu.my/id/eprint/15805

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