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 processing (NLP), and is crucial since it enables the interpretation of intricate narratives and connections contained within vast amounts of written information. This paper proposed a hybrid framework for causality detection and extraction from nuclear licensee event reports. The main contributions include: (1) we compiled an LER corpus with 20,129 text samples for causality analysis, (2) developed an interactive tool for labeling cause effect pairs, (3) built a deep-learning-based approach for causal relation detection, and (4) developed a knowledge based cause-effect extraction approach.
Abstract (translated)
行业范围内的核电站运行经验是进行可靠性风险模型参数估计的关键原始数据来源。大量的操作经验信息涉及故障事件，并存储为包含无结构数据的报告，如叙述。事件报告对理解故障是如何启动和传播的至关重要，包括众多因果关系。使用深度学习提取因果关系代表了自然语言处理（NLP）领域的一个显著前沿，并且对解释大量书面信息中的复杂叙述和联系至关重要。本文提出了一种用于从核电站业主事件报告中检测和提取因果性的混合框架。主要贡献包括：（1）我们编写了包含20,129个文本样本的LR语料库，用于进行因果性分析；（2）开发了一个用于标记因果效应对的工具；（3）基于深度学习构建了因果关系检测方法；（4）开发了基于知识的因果关系提取方法。
URL
https://arxiv.org/abs/2404.05656
