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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 vitalinformation from the deluge of reports and documents produced by thehumanitarian sector. However, the performance and biases of existingstate-of-the-art information extraction tools are unknown. In this work, wedevelop annotated resources to fine-tune the popular Named Entity Recognition(NER) tools Spacy and roBERTa to perform geotagging of humanitarian texts. Wethen propose a geocoding method FeatureRank which links the candidate locationsto the GeoNames database. We find that not only does the humanitarian-domaindata improves the performance of the classifiers (up to F1 = 0.92), but it alsoalleviates some of the bias of the existing tools, which erroneously favorlocations in the Western countries. Thus, we conclude that more resources fromnon-Western documents are necessary to ensure that off-the-shelf NER systemsare suitable for the deployment in the humanitarian sector.

Leave no Place Behind: Improved Geolocation in Humanitarian Documents