# Location reference recognition from texts: A survey and comparison

arXiv: https://arxiv.org/abs/2207.01683 · HTML: https://mlsanity.com/paper/2207.01683/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 207 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](https://mlsanity.com/paper/1410.3791/) (2014)
- [NeuroNER: an easy-to-use program for named-entity recognition based on neural networks](https://mlsanity.com/paper/1705.05487/) (2017)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)
- [Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers](https://mlsanity.com/paper/2007.04524/) (2020)
- [A Coherent Unsupervised Model for Toponym Resolution](https://mlsanity.com/paper/1805.01952/) (2018)
- [Utilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities](https://mlsanity.com/paper/2007.09368/) (2020)
- [How do people describe locations during a natural disaster: an analysis of tweets from Hurricane Harvey](https://mlsanity.com/paper/2009.12914/) (2020)
- [DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets](https://mlsanity.com/paper/1901.07005/) (2019)
- [Social Media for Emergency Rescue: An Analysis of Rescue Requests on Twitter during Hurricane Harvey](https://mlsanity.com/paper/2111.07187/) (2021)
- [SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies](https://mlsanity.com/paper/1801.07757/) (2018)
- [Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform](https://mlsanity.com/paper/2007.07455/) (2020)
- [Using social network and semantic analysis to analyze online travel forums and forecast tourism demand](https://mlsanity.com/paper/2105.07727/) (2021)
- [Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models](https://mlsanity.com/paper/1708.03105/) (2017)

## Built on by (most cited first, 17 of 17)

- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach](https://mlsanity.com/paper/2512.20056/) (2025)
- [Explicit Interaction for Fusion-Based Place Recognition](https://mlsanity.com/paper/2402.17264/) (2024)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization](https://mlsanity.com/paper/2607.24856/) (2026)
- [Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation](https://mlsanity.com/paper/2305.13844/) (2023)
- [Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German](https://mlsanity.com/paper/2605.03414/) (2026)
- [Toward building next-generation Geocoding systems: a systematic review](https://mlsanity.com/paper/2503.18888/) (2025)
- [Where Do Images Come From? Analyzing Captions to Geographically Profile Datasets](https://mlsanity.com/paper/2602.09775/) (2026)
- [Interpretable Perception and Reasoning for Audiovisual Geolocation](https://mlsanity.com/paper/2603.05708/) (2026)
- [Assessing socio-economic climate impacts from text data](https://mlsanity.com/paper/2605.20793/) (2026)
- [Exploring the law of text geographic information](https://mlsanity.com/paper/2309.00180/) (2023)
- [Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias](https://mlsanity.com/paper/2505.01754/) (2025)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [ATD-Trans: A Geographically Grounded Japanese-English Travelogue Translation Dataset](https://mlsanity.com/paper/2605.12933/) (2026)
- [Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government](https://mlsanity.com/paper/2609.11674/) (2026)

## Abstract (arXiv)

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs.