Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework

TL;DR

This paper presents a hybrid framework combining deep learning and knowledge-based methods for extracting causal relations from unstructured nuclear event reports, aiding reliability analysis.

Contribution

It introduces a new corpus, an interactive labeling tool, and a hybrid approach for causal relation extraction from nuclear incident reports.

Findings

Created a corpus with 20,129 samples for causality analysis

Developed a deep learning model for causal relation detection

Implemented a knowledge-based cause-effect extraction method

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.