# Regressing Location on Text for Probabilistic Geocoding

arXiv: https://arxiv.org/abs/2107.00080 · HTML: https://mlsanity.com/paper/2107.00080/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [Spatial Language Representation with Multi-Level Geocoding](https://mlsanity.com/paper/2008.09236/) (2020)
- [Lost in Space: Geolocation in Event Data](https://mlsanity.com/paper/1611.04837/) (2016)

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

- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report](https://mlsanity.com/paper/2108.07865/) (2021)
- [Mordecai 3: A Neural Geoparser and Event Geocoder](https://mlsanity.com/paper/2303.13675/) (2023)
- [Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base](https://mlsanity.com/paper/2212.11856/) (2022)
- [Transformer Based Geocoding](https://mlsanity.com/paper/2301.01170/) (2023)
- [ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs](https://mlsanity.com/paper/2604.21357/) (2026)

## Abstract (arXiv)

Text data are an important source of detailed information about social and political events. Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations. One of these sub-tasks is geocoding: predicting the geographic coordinates associated with events or locations described by a given text. We present an end-to-end probabilistic model for geocoding text data. Additionally, we collect a novel data set for evaluating the performance of geocoding systems. We compare the model-based solution, called ELECTRo-map, to the current state-of-the-art open source system for geocoding texts for event data. Finally, we discuss the benefits of end-to-end model-based geocoding, including principled uncertainty estimation and the ability of these models to leverage contextual information.