Developing a web tool for analysis of scientific literature to gain up-to-date information related to COVID-19
- Context: COVID-19 literature published from January to the middle of May was more than 23,000 papers and doubling every 20 days.
- Problem: Experts across domains have an urgent need for information up-to-date and pertinent to their respective fields but they struggle to keep up with the publication volume.
- Hypothesis: Exploring literature and discovering knowledge in it can be facilitated by an environment that exposes a semantically enriched version of the literature.
- Build an environment for analysis of scientific literature related to COVID-19, which offers various modalities for aggregation of information across multiple papers.
- Our aim is to provide a web tool where COVID-19 literature is enriched by:
– NER annotations (provided by OGER)
– text and NER searches
– a network of semantically connected publications (by sentence similarity)
– extractive summarization
– a reading strategy powered by semantic hyperlinks among sentences.