Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid
  Framework
Sohag Rahman Sai Zhang Min Xian Shoukun Sun Fei Xu Zhegang Ma
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.