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Automatic Classification of Industrial Accident Causes using NLP: A Case Study in eMARS

Valerio Cozzani B. Fabiano G. Reniers J. A. García Daniel Fajardo-López Jacqueline Mesa Rafael Amaya-Gómez
· 0 citations · 16 references

TL;DR

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.

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