How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?

SNEToolkit: Spatial named entities disambiguation toolkit

“…This approach provides an in-depth exploration of text-based geolocation prediction systems, delivering robust and practical solutions. In [15] authors discuss various toponym resolution techniques, including entity linking and toponym resolution using traditional machine learning algorithms based on feature engineering and deep learning algorithms. The paper proposes a spatial clustering-based voting approach that combines several individual approaches to improve the robustness and generalizability of the techniques.…”

“…Nonetheless, it is important to acknowledge that hybrid methodologies, akin to the one put forth in Ref. [15], have been found to be capable of delivering superior performance across diverse types of documents. Such an approach consolidates the strengths of various techniques, thereby enhancing the effectiveness of spatial named entity recognition and resolution irrespective of the document context.…”