Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework

arXiv:2404.05656v1 Announce Type: cross
Abstract: Industry-wide nuclear power plant operating experience is a critical source of raw data for performing parameter estimations in reliability and risk models. Much operating experience information pertains to failure events and is stored as reports containing unstructured data, such as narratives. Event reports are essential for understanding how failures are initiated and propagated, including the numerous causal relations involved. Causal relation extraction using deep learning represents a significant frontier in the field of natural language …

abstract arxiv causality cs.ai cs.cl cs.lg data estimations event events experience extraction failure framework hybrid industry information nuclear nuclear power power raw reliability reports risk type unstructured unstructured data