@inproceedings{nguyen-etal-2020-pandemic,
title = "Pandemic Literature Search: Finding Information on {COVID}-19",
author = "Nguyen, Vincent and
Rybinski, Maciek and
Karimi, Sarvnaz and
Xing, Zhenchang",
editor = "Kim, Maria and
Beck, Daniel and
Mistica, Meladel",
booktitle = "Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association",
month = dec,
year = "2020",
address = "Virtual Workshop",
publisher = "Australasian Language Technology Association",
url = "https://aclanthology.org/2020.alta-1.11",
pages = "92--97",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Pandemic Literature Search: Finding Information on COVID-19
%A Nguyen, Vincent
%A Rybinski, Maciek
%A Karimi, Sarvnaz
%A Xing, Zhenchang
%Y Kim, Maria
%Y Beck, Daniel
%Y Mistica, Meladel
%S Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
%D 2020
%8 December
%I Australasian Language Technology Association
%C Virtual Workshop
%F nguyen-etal-2020-pandemic
%X 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.
%U https://aclanthology.org/2020.alta-1.11
%P 92-97
Markdown (Informal)
[Pandemic Literature Search: Finding Information on COVID-19](https://aclanthology.org/2020.alta-1.11) (Nguyen et al., ALTA 2020)
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.