Abstract
Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this we explore the challenging task of geo-entity linking for noisy, multilingual social media data. There are few open-source multilingual geo-entity linking tools available and existing ones are often rule-based, which break easily in social media settings, or LLM-based, which are too expensive for large-scale datasets. We present a method which represents real-world locations as averaged embeddings from labeled user-input location names and allows for selective prediction via an interpretable confidence score. We show that our approach improves geo-entity linking on a global and multilingual social media dataset, and discuss progress and problems with evaluating at different geographic granularities.- Anthology ID:
- 2024.nlpcss-1.7
- Volume:
- Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Dallas Card, Anjalie Field, Dirk Hovy, Katherine Keith
- Venues:
- NLP+CSS | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 86–98
- Language:
- URL:
- https://aclanthology.org/2024.nlpcss-1.7
- DOI:
- Cite (ACL):
- Tessa Masis and Brendan O’Connor. 2024. Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input. In Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024), pages 86–98, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input (Masis & O’Connor, NLP+CSS-WS 2024)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2024.nlpcss-1.7.pdf