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The method demonstrates semantic processing of emergency incident texts, highlighting Bayesian optimisation for critical infrastructure industries.
The paper proposes a scenariooptimisation method for semantic processing of emergency incident texts and demonstrates its validation on the NRC Event Notifications corpus. The pipeline includes: preprocessing and LDAthematisation; autoannotation of topics by a transformational model; PLS target factorisation; regularised risk regression with multicriteria validation (goodness_ of_fit, scale_errors, cv_metrics); Bayesian optimisation of management «levers»; construction of a graph of thematic relationships and generation of recommendations ready for loading into EAM/CMMS. Based on the scenario comparison, the PLS+Ridge (L/R) and Ridge Gamma (L/R) configurations lead the way. The optimisation step shows the dominance of the theme «unauthorised access» in reducing the integral risk; the graph reveals the bridging role of «pollutant discharges» and the coupling with «equipment accidents». The result is a reproducible translation of textual analytics into a preventive and corrective action plan. The method solves the drift and interpretability problem, provides traceability and integration with EAM/CMMS, and can be scaled to other critical infrastructure industries.
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Rodionov et al. (2025) studied this question.
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