  We present a multi-level geocoding model (MLG) that learns to associate texts
to geographic locations. The Earth's surface is represented using space-filling
curves that decompose the sphere into a hierarchy of similarly sized,
non-overlapping cells. MLG balances generalization and accuracy by combining
losses across multiple levels and predicting cells at each level
simultaneously. Without using any dataset-specific tuning, we show that MLG
obtains state-of-the-art results for toponym resolution on three English
datasets. Furthermore, it obtains large gains without any knowledge base
metadata, demonstrating that it can effectively learn the connection between
text spans and coordinates - and thus can be extended to toponymns not present
in knowledge bases.
