# Leave no Place Behind: Improved Geolocation in Humanitarian Documents

arXiv: https://arxiv.org/abs/2309.02914 · HTML: https://mlsanity.com/paper/2309.02914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [NeuroNER: an easy-to-use program for named-entity recognition based on neural networks](https://mlsanity.com/paper/1705.05487/) (2017)
- [Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies](https://mlsanity.com/paper/2109.06974/) (2021)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Where in the World are You? Geolocation and Language Identification in Twitter](https://mlsanity.com/paper/1308.0683/) (2013)
- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response](https://mlsanity.com/paper/2210.04573/) (2022)
- [Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide](https://mlsanity.com/paper/2002.11645/) (2020)
- [Developing Annotated Resources for Internal Displacement Monitoring](https://mlsanity.com/paper/2104.05459/) (2021)
- [Disambiguating fine-grained place names from descriptions by clustering](https://mlsanity.com/paper/1808.05946/) (2018)

## Built on by (most cited first, 5 of 5)

- [Evaluation of Geographical Distortions in Language Models](https://mlsanity.com/paper/2404.17401/) (2024)
- [Quantitative Information Extraction from Humanitarian Documents](https://mlsanity.com/paper/2408.04941/) (2024)
- [A Large-Language-Model Framework for Automated Humanitarian Situation Reporting](https://mlsanity.com/paper/2512.19475/) (2025)
- [Large Language Models for Geolocation Extraction in Humanitarian Crisis Response](https://mlsanity.com/paper/2602.08872/) (2026)
- [Assessing socio-economic climate impacts from text data](https://mlsanity.com/paper/2605.20793/) (2026)

## Abstract (arXiv)

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.