  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.
