Extracting information from narratives: an application to aviation safety reports

Aviation safety reports are the best available source of information explaining why a flight incident happened. However, stream of consciousness permeates the narratives making the automation of the information extraction task difficult. We propose an approach and infrastructure based on a common pattern specification language to capture relevant information via normalized template expression matching in context. Template expression matching handles variants of multi-word expressions. Normalization improves the likelihood of correct hits by standardizing and cleaning the vocabulary used in narratives. Checking for the presence of negative modifiers in the proximity of a potential hit reduces the chance of false hits. We present the above approach in the context of a specific application that is the extraction of human performance factors from NASA ASRS reports. While knowledge infusion from experts plays a critical role during the learning phase, early results show that in a production mode, the automated process provides information that is consistent with analyses by human subjects.