Natural Language Processing of Aviation Occurrence Reports for Safety Management

TL;DR

This paper investigates NLP techniques to automate and enhance the analysis of aviation safety reports, including classification, topic extraction, and cause text generation, aiming to reduce manual effort and improve consistency.

Contribution

It introduces NLP methods for automatic report labeling, topic modeling, and cause text generation, addressing challenges like interrater variability and incomplete taxonomy coverage.

Findings

Transformer-based classifiers effectively automate report labeling.

Topic models reveal prevalent themes in safety reports.

Summarization models show promise in generating probable cause texts.

Abstract

Occurrence reporting is a commonly used method in safety management systems to obtain insight in the prevalence of hazards and accident scenarios. In support of safety data analysis, reports are often categorized according to a taxonomy. However, the processing of the reports can require significant effort from safety analysts and a common problem is interrater variability in labeling processes. Also, in some cases, reports are not processed according to a taxonomy, or the taxonomy does not fully cover the contents of the documents. This paper explores various Natural Language Processing (NLP) methods to support the analysis of aviation safety occurrence reports. In particular, the problems studied are the automatic labeling of reports using a classification model, extracting the latent topics in a collection of texts using a topic model and the automatic generation of probable cause texts. Experimental results showed that (i) under the right conditions the labeling of occurrence reports can be effectively automated with a transformer-based classifier, (ii) topic modeling can be useful for finding the topics present in a collection of reports, and (iii) using a summarization model can be a promising direction for generating probable cause texts.