NAACL 2024short2 citations
Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries
Zeyu Zhang, Egoitz Laparra, Steven Bethard
Abstract
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.
BibTeX
@inproceedings{zhang-etal-2024-improving-toponym,
title = "Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries",
author = "Zhang, Zeyu and
Laparra, Egoitz and
Bethard, Steven",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.3/",
doi = "10.18653/v1/2024.naacl-short.3",
pages = "35--44"
}