diff --git "a/s\303\251minaires/session9_dec22/README.md" "b/s\303\251minaires/session9_dec22/README.md" index 2587b384e0032b72754359f5f980710e24dcbee9..3eee38e6c3502448bdbb97e2e5717da3b68bb2a8 100644 --- "a/s\303\251minaires/session9_dec22/README.md" +++ "b/s\303\251minaires/session9_dec22/README.md" @@ -9,12 +9,18 @@ Inscription : [lien à venir](#) ## Programme -### Titre à venir... +### 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(German Aerospace Center) +A vast amount of geographic information exists in natural language texts, such as tweets and historical documents.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 The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world.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.