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Filtering by: Creator/Author Hagedorn, Philip Publisher University of Cincinnati

COVID 19 literature NLP models – Viral outbreak topic tuning

Type:

Dataset

Description/Abstract:

The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.

Creator/Author:

Koshoffer, Amy; Wu, Danny; Latessa, Jenny; Kannayyagar, Suraj; Luken, Sally; McCabe, Erin; Edgerton, Ezra; Washington, Dorcas; Lee, James; Powers, Margaret, and Hagedorn, Philip

Submitter:

Amy Koshoffer

Date Uploaded:

10/30/2020

Date Modified:

11/05/2020

Date Created:

2020-07

License:

CC0 1.0 Universal