Pandemic Literature Search: Finding Information on COVID-19
Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, Zhenchang Xing
Abstract
Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. We investigate how to better rank information for pandemic information retrieval. We experiment with different ranking algorithms and propose a novel end-to-end method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search. This work could lead to a search system that aids scientists, clinicians, policymakers and others in finding reliable answers from the scientific literature.- Anthology ID:
- 2020.alta-1.11
- Volume:
- Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2020
- Address:
- Virtual Workshop
- Editors:
- Maria Kim, Daniel Beck, Meladel Mistica
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 92–97
- Language:
- URL:
- https://aclanthology.org/2020.alta-1.11
- DOI:
- Cite (ACL):
- Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, and Zhenchang Xing. 2020. Pandemic Literature Search: Finding Information on COVID-19. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 92–97, Virtual Workshop. Australasian Language Technology Association.
- Cite (Informal):
- Pandemic Literature Search: Finding Information on COVID-19 (Nguyen et al., ALTA 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.alta-1.11.pdf
- Data
- CORD-19, MS MARCO