Leave no Place Behind: Improved Geolocation in Humanitarian Documents
Enrico M. Belliardo Kyriaki Kalimeri Yelena Mejova
Geographical location is a crucial element of humanitarian response,
outlining vulnerable populations, ongoing events, and available resources.
Latest developments in Natural Language Processing may help in extracting vital
information from the deluge of reports and documents produced by the
humanitarian sector. However, the performance and biases of existing
state-of-the-art information extraction tools are unknown. In this work, we
develop annotated resources to fine-tune the popular Named Entity Recognition
(NER) tools Spacy and roBERTa to perform geotagging of humanitarian texts. We
then propose a geocoding method FeatureRank which links the candidate locations
to the GeoNames database. We find that not only does the humanitarian-domain
data improves the performance of the classifiers (up to F1 = 0.92), but it also
alleviates some of the bias of the existing tools, which erroneously favor
locations in the Western countries. Thus, we conclude that more resources from
non-Western documents are necessary to ensure that off-the-shelf NER systems
are suitable for the deployment in the humanitarian sector.