@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",
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 \url{https://github.com/clulab/geonorm}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries
%A Zhang, Zeyu
%A Laparra, Egoitz
%A Bethard, Steven
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhang-etal-2024-improving-toponym
%X 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.
%R 10.18653/v1/2024.naacl-short.3
%U https://aclanthology.org/2024.naacl-short.3
%U https://doi.org/10.18653/v1/2024.naacl-short.3
%P 35-44
Markdown (Informal)
[Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries](https://aclanthology.org/2024.naacl-short.3) (Zhang et al., NAACL 2024)
ACL