# Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs

arXiv: https://arxiv.org/abs/2503.01386 · HTML: https://mlsanity.com/paper/2503.01386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [DART: Dropouts meet Multiple Additive Regression Trees](https://mlsanity.com/paper/1505.01866/) (2015)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)
- [Towards Real-Time, Country-Level Location Classification of Worldwide Tweets](https://mlsanity.com/paper/1604.07236/) (2016)
- [A survey of location inference techniques on Twitter](https://mlsanity.com/paper/1701.03639/) (2017)
- [Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction](https://mlsanity.com/paper/1902.03237/) (2019)
- [Home Location Identification of Twitter Users](https://mlsanity.com/paper/1403.2345/) (2014)
- [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)

## Abstract (arXiv)

Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10k event-related tweets, achieving F1=0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.