From 3adbaad374f69e2107ab4c679d2726e60820247c Mon Sep 17 00:00:00 2001 From: Ludovic Moncla <moncla.ludovic@gmail.com> Date: Fri, 2 Dec 2022 11:16:02 +0100 Subject: [PATCH] Update README.md --- "s\303\251minaires/session9_dec22/README.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/s\303\251minaires/session9_dec22/README.md" "b/s\303\251minaires/session9_dec22/README.md" index 545389e..5f1813e 100644 --- "a/s\303\251minaires/session9_dec22/README.md" +++ "b/s\303\251minaires/session9_dec22/README.md" @@ -16,7 +16,7 @@ Xuke Hu (German Aerospace Center) A vast amount of geospatial information exists in natural language texts (e.g., social media posts, website texts, and historical archives) in the form of toponyms, place names, and location descriptions. Extracting geographic information from texts is named geoparsing, which is beneficial not only for scientific studies, such as sociolinguistics and spatial humanities but can also contribute to various practical applications, such as disaster management, urban planning, and disease surveillance. In the presentation, I will share our latest findings in the two sub-tasks of geoparsing: toponym recognition and toponym resolution. Specifically, I will introduce our proposed approaches for the two sub-tasks and compare them with numerous existing ones based on many datasets. -#### Related publications +*Related publications* [1] Hu, X., Al-Olimat, H.S., Kersten, J., Wiegmann, M., Klan, F., Sun, Y. and Fan, H., 2022. GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules. International Journal of Geographical Information Science, 36(2), pp.310-337. -- GitLab