### How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?
Xuke Hu (German Aerospace Center)
A vast amount of geographic information exists in natural language texts, such as tweets and historical documents. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. In this report, I will share our latest findings in toponym disambiguation. Specifically, we proposed a spatial clustering-based voting approach that combines several individual approaches to improve
SOTA performance in terms of robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches (especially deep learning-based ones) on 12 public datasets, including several highly ambiguous
and challenging datasets (e.g., WikToR and CLDW). The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world. The results prove the generalizability
and robustness of the voting approach. Also, the voting ensemble drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways.
Résumé à venir...
#### Reference
Hu, X., Sun, Y., Kersten, J., Zhou, Z., Klan, F. and Fan, H., 2022. How can voting mechanisms improve the robustness and generalizability of toponym disambiguation? arXiv preprint arXiv:2209.08286.
