Automatic Classification of Industrial Accident Causes using NLP: A Case Study in eMARS
This study proposes a lightweight Natural Language Processing (NLP) pipeline to automatically classify the primary cause of major accidents using the European eMARS database and shows that Word2Vec+SVM provides the strongest and most stable baseline on the full labelled set, while SBERT performance improves markedly under higher label fidelity.
Valerio Cozzani, B. Fabiano, G. Reniers et al.
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