@inproceedings{bonial-etal-2020-infoforager,
title = "{I}nfo{F}orager: Leveraging Semantic Search with {AMR} for {COVID}-19 Research",
author = "Bonial, Claire and
Lukin, Stephanie M. and
Doughty, David and
Hill, Steven and
Voss, Clare",
editor = "Xue, Nianwen and
Bos, Johan and
Croft, William and
Haji{\v{c}}, Jan and
Huang, Chu-Ren and
Oepen, Stephan and
Palmer, Martha and
Pustejovsky, James",
booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
month = dec,
year = "2020",
address = "Barcelona Spain (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.dmr-1.7/",
pages = "67--77",
abstract = "This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task."
}
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<abstract>This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.</abstract>
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%0 Conference Proceedings
%T InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research
%A Bonial, Claire
%A Lukin, Stephanie M.
%A Doughty, David
%A Hill, Steven
%A Voss, Clare
%Y Xue, Nianwen
%Y Bos, Johan
%Y Croft, William
%Y Hajič, Jan
%Y Huang, Chu-Ren
%Y Oepen, Stephan
%Y Palmer, Martha
%Y Pustejovsky, James
%S Proceedings of the Second International Workshop on Designing Meaning Representations
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona Spain (online)
%F bonial-etal-2020-infoforager
%X This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.
%U https://aclanthology.org/2020.dmr-1.7/
%P 67-77
Markdown (Informal)
[InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research](https://aclanthology.org/2020.dmr-1.7/) (Bonial et al., DMR 2020)
ACL