Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries

Zhang, Zeyu & Laparra, Egoitz & Bethard, Steven. (2024). Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries. 35-44. 10.18653/v1/2024.naacl-short.3.

Geocoding is the task of converting location mentions in text into structured geospatial data. We propose a new prompt-based paradigm for geocoding, where the machine learning algorithm encodes only the location mention and its context. We design a transformer network for predicting the country, state, and feature class of a location mention, and a deterministic algorithm that leverages the country, state, and feature class predictions as constraints in a search for compatible entries in the ontology. Our architecture, GeoPLACE, achieves new state-of-the-art performance on multiple datasets. Code and models are available at https://github. com/clulab/geonorm.