Constructing a Ship Collision Accident Dataset Using Template-Based Corpus and Named Entity Recognition
Year: 2026
Type: article
Abstract: Ship collisions pose substantial risks to maritime safety, causing vessel damage, casualties, and environmental impacts. Efficient extraction and analysis of key navigational and causal information from accident reports are important for risk assessment and decision support. This study proposes a framework for synthetic data generation, DistilBERT-based named entity recognition, and structured dataset construction for ship collision accidents. Using a template-based method, 56,000 annotated sentences were generated, covering navigational elements and causal factor trigger phrases. The fine-tuned DistilBERT model showed good performance on both synthetic and real accident reports. Statistical and co-occurrence analyses further indicated that failure to maintain proper lookout, failure to take effective evasive action, and failure to maintain safe speed were the main contributing factors across different environments and accident severity levels. Based on the extraction results, a standardized structured dataset was constructed to support subsequent causal analysis, dynamic risk modeling, and collision risk prediction. The study shows that combining template-based data synthesis with Transformer-based named entity recognition is a feasible approach for extracting information from maritime accident reports and transforming unstructured text into structured datasets.
DOI: 10.3390/jmse14090832
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G4390389757, G932897937
OpenAlex ID: W7160186790