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COVID-19 multidimensional kaggle literature organization
21st ACM Symposium on Document Engineering, DocEng 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1394246
ABSTRACT
The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As a result, a surge of new COVID-19 related research has followed suit. The growing number of publications requires document organization methods to identify relevant information. In this paper, we expand upon our previous work with clustering the CORD-19 dataset by applying multi-dimensional analysis methods. Tensor factorization is a powerful unsupervised learning method capable of discovering hidden patterns in a document corpus. We show that a higher-order representation of the corpus allows for the simultaneous grouping of similar articles, relevant journals, authors with similar research interests, and topic keywords. These groupings are identified within and among the latent components extracted via tensor decomposition. We further demonstrate the application of this method with a publicly available interactive visualization of the dataset. © 2021 ACM.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 21st ACM Symposium on Document Engineering, DocEng 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 21st ACM Symposium on Document Engineering, DocEng 2021 Year: 2021 Document Type: Article