A semi-supervised approach to multi-label classification of NOTAMs using BERT

Notices to Air Missions (NOTAMs) are key for the safe operation of commercial and civil aviation flights around the globe, as they provide up-to-date information on any disturbance on aerodromes and in the airspace. In this work, we propose a framework to train bidirectional encoder representations from transformers (BERT) to assign labels to NOTAM messages using a multi-label classification approach where each NOTAM can be assigned multiple labels. To deal with the scarcity of labeled data for this task, we propose a semi-supervised learning framework using the MixMatch algorithm to allow the leveraging of unla- beled NOTAMs, and reduce the need for expert labeled NOTAMs. We demonstrate that the MixMatch algorithm combined with focal loss improves the performance of BERT on the multi- label classification of NOTAMs, with the micro-averaged F1-score improving from 0.93 to 0.96 on a publicly available test dataset.