Improving Toponym Resolution with Better Candidate Generation,
  Transformer-based Reranking, and Two-Stage Resolution
Zeyu Zhang Steven Bethard
Geocoding is the task of converting location mentions in text into structured
data that encodes the geospatial semantics. We propose a new architecture for
geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to
generate a list of candidate entries from the geospatial ontology. Then it
reranks the candidate entries using a transformer-based neural network that
incorporates information from the ontology such as the entry's population. This
generate-and-rerank process is applied twice: first to resolve the less
ambiguous countries, states, and counties, and second to resolve the remaining
location mentions, using the identified countries, states, and counties as
context. Our proposed toponym resolution framework achieves state-of-the-art
performance on multiple datasets. Code and models are available at
\url{https://github.com/clulab/geonorm}.