# Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution

arXiv: https://arxiv.org/abs/2305.11315 · HTML: https://mlsanity.com/paper/2305.11315/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 13 of 13)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)
- [Global Entity Disambiguation with BERT](https://mlsanity.com/paper/1909.00426/) (2019)
- [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)
- [Improving Entity Disambiguation by Reasoning over a Knowledge Base](https://mlsanity.com/paper/2207.04106/) (2022)
- [Exploiting Text and Network Context for Geolocation of Social Media Users](https://mlsanity.com/paper/1506.04803/) (2015)
- [A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching](https://mlsanity.com/paper/2009.08114/) (2020)
- [Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media](https://mlsanity.com/paper/1707.04244/) (2017)

## Built on by (most cited first, 7 of 7)

- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation](https://mlsanity.com/paper/2305.13844/) (2023)
- [Large Language Models for Geolocation Extraction in Humanitarian Crisis Response](https://mlsanity.com/paper/2602.08872/) (2026)
- [RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News Articles](https://mlsanity.com/paper/2501.11440/) (2025)
- [From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search](https://mlsanity.com/paper/2608.28965/) (2026)
- [From UNDRR Reports to Event Records: Schema-Constrained LLM Extraction of Georeferenced Disasters](https://mlsanity.com/paper/2609.23853/) (2026)

## Abstract (arXiv)

Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology. Then it reranks the candidate entries using a transformer-based neural network that incorporates information from the ontology such as the entry's population. This generate-and-rerank process is applied twice: first to resolve the less ambiguous countries, states, and counties, and second to resolve the remaining location mentions, using the identified countries, states, and counties as context. Our proposed toponym resolution framework achieves state-of-the-art performance on multiple datasets. Code and models are available at urlhttps://github.com/clulab/geonorm.