# Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input

arXiv: https://arxiv.org/abs/2404.18784 · HTML: https://mlsanity.com/paper/2404.18784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation](https://mlsanity.com/paper/2210.12213/) (2022)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)
- [A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://mlsanity.com/paper/1910.12941/) (2019)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers](https://mlsanity.com/paper/2007.04524/) (2020)
- [Confounds and Consequences in Geotagged Twitter Data](https://mlsanity.com/paper/1506.02275/) (2015)
- [Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks](https://mlsanity.com/paper/1708.04358/) (2017)
- [A Coherent Unsupervised Model for Toponym Resolution](https://mlsanity.com/paper/1805.01952/) (2018)
- [Changes in Twitter geolocations: Insights and suggestions for future usage](https://mlsanity.com/paper/2108.12251/) (2021)
- [Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution](https://mlsanity.com/paper/2305.11315/) (2023)
- [Exploiting Text and Network Context for Geolocation of Social Media Users](https://mlsanity.com/paper/1506.04803/) (2015)
- [A Large-Scale Empirical Study of Geotagging Behavior on Twitter](https://mlsanity.com/paper/1908.10948/) (2019)
- [A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching](https://mlsanity.com/paper/2009.08114/) (2020)
- [Privacy risk in GeoData: A survey](https://mlsanity.com/paper/2402.03612/) (2024)

## Built on by (most cited first, 4 of 4)

- [GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding](https://mlsanity.com/paper/2408.11366/) (2024)
- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search](https://mlsanity.com/paper/2608.28965/) (2026)

## Abstract (arXiv)

Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this paper we explore the challenging task of geo-entity linking for noisy, multilingual social media data. There are few open-source multilingual geo-entity linking tools available and existing ones are often rule-based, which break easily in social media settings, or LLM-based, which are too expensive for large-scale datasets. We present a method which represents real-world locations as averaged embeddings from labeled user-input location names and allows for selective prediction via an interpretable confidence score. We show that our approach improves geo-entity linking on a global and multilingual social media dataset, and discuss progress and problems with evaluating at different geographic granularities.