Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework
This work addresses the need for automated causality analysis in nuclear safety reports, which is incremental as it applies existing NLP methods to a new domain-specific dataset.
The paper tackled the problem of extracting causal relations from unstructured nuclear licensee event reports by proposing a hybrid framework, resulting in a compiled corpus of 20,129 text samples and tools for detection and extraction.
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.