@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://preview.aclanthology.org/jlcl-multiple-ingestion/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}."
}