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.- Anthology ID:
- 2024.naacl-short.3
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 35–44
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.3
- DOI:
- Cite (ACL):
- Zeyu Zhang, Egoitz Laparra, and Steven Bethard. 2024. Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 35–44, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries (Zhang et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2024.naacl-short.3.pdf