HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response

Fekih, Selim; Tamagnone, Nicolò; Minixhofer, Benjamin; Shrestha, Rabindra Man; Contla, Ximena; Oglethorpe, Ewan; Rekabsaz, Navid

doi:10.18653/v1/2022.findings-emnlp.321

Cited by 1 publication

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“…We employ data from the HumSet database [9], originated from the multi-organizational platform (DEEP) introduced earlier. Each entry consists of a relevant excerpt from a document, annotated with the humanitarian analysis framework categories.…”

Section: Data Collectionmentioning

confidence: 99%

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Belliardo,

Kalimeri,

Mejova

2023

Proceedings of the 2023 ACM Conference on Information Technology for Social Good

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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 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. CCS CONCEPTS• Social and professional topics → Geographic characteristics; • Information systems → Data mining; Information systems applications.

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Section: Data Collectionmentioning

confidence: 99%