Abstract
Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.- Anthology ID:
- 2022.lrec-1.776
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 7164–7173
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.776
- DOI:
- Cite (ACL):
- Sarthak Khanal, Maria Traskowsky, and Doina Caragea. 2022. Identification of Fine-Grained Location Mentions in Crisis Tweets. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7164–7173, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Identification of Fine-Grained Location Mentions in Crisis Tweets (Khanal et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.776.pdf