Dataset
COVID 19 literature NLP models – Viral outbreak topic tuning Open Access Deposited
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.
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Items
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covid_q2_topics_tuning.csv | 2020-10-30 | Open Access |
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ebola_m2_topics_tuning.csv | 2020-10-30 | Open Access |
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10k_eval_avgs.txt | 2020-10-30 | Open Access |
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covid_q1_topics_tuning.csv | 2020-10-30 | Open Access |
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hiv_m2_topics_tuning.csv | 2020-10-30 | Open Access |
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hyperparam_tuning_on_10k_rand.csv | 2020-10-30 | Open Access |
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full_tuning_results.csv | 2020-10-30 | Open Access |
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h1n1_m2_topics_tuning.csv | 2020-10-30 | Open Access |
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zika_m2_topics_tuning.csv | 2020-10-30 | Open Access |
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mers_m2_topics_tuning.csv | 2020-10-30 | Open Access |
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Permanent link to this page: https://scholar.uc.edu/show/pk02cc123