COVID-19 Kaggle Literature Organization

Keywords:

COVID-19, dimensionality reduction, clustering, document visualization

Citation:

Maksim Ekin Eren, Nick Solovyev, Edward Raff, Charles Nicholas, and Ben Johnson. 2020. COVID-19 Kaggle Literature Organization. In Proceedings of the ACM Symposium on Document Engineering 2020 (DocEng ‘20). Association for Computing Machinery, New York, NY, USA, Article 15, 1–4. DOI:https://doi.org/10.1145/3395027.3419591

BibTeX:

@inproceedings{10.1145/3395027.3419591, 
  author = {Eren, Maksim Ekin and Solovyev, Nick and Raff, Edward and Nicholas, Charles and Johnson, Ben}, 
  title = {COVID-19 Kaggle Literature Organization}, 
  year = {2020}, 
  isbn = {9781450380003}, 
  publisher = {Association for Computing Machinery}, 
  address = {New York, NY, USA}, 
  url = {https://doi.org/10.1145/3395027.3419591}, 
  doi = {10.1145/3395027.3419591}, 
  abstract = {The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 dataset to present a publicly available proof of concept.}, 
  booktitle = {Proceedings of the ACM Symposium on Document Engineering 2020}, 
  articleno = {15}, 
  numpages = {4}, 
  keywords = {document visualization, dimensionality reduction, clustering, COVID-19}, 
  location = {Virtual Event, CA, USA}, 
  series = {DocEng '20} 
}