Abstract
Text data are an important source of detailed information about social and political events. Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations. One of these sub-tasks is geocoding: predicting the geographic coordinates associated with events or locations described by a given text. I present an end-to-end probabilistic model for geocoding text data. Additionally, I collect a novel data set for evaluating the performance of geocoding systems. I compare the model-based solution, called ELECTRo-map, to the current state-of-the-art open source system for geocoding texts for event data. Finally, I discuss the benefits of end-to-end model-based geocoding, including principled uncertainty estimation and the ability of these models to leverage contextual information.- Anthology ID:
- 2021.case-1.8
- Volume:
- Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- CASE
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–57
- Language:
- URL:
- https://aclanthology.org/2021.case-1.8
- DOI:
- 10.18653/v1/2021.case-1.8
- Cite (ACL):
- Benjamin J. Radford. 2021. Regressing Location on Text for Probabilistic Geocoding. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 53–57, Online. Association for Computational Linguistics.
- Cite (Informal):
- Regressing Location on Text for Probabilistic Geocoding (Radford, CASE 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.case-1.8.pdf