For starters, let's get you familiar with some of the terms we are using. NER is an acronym for Named Entity Recognition, which is a deep learning text analytics subdomain where terms and phrases are identified within text and classified into categorical (aka entity) types (proteins, cell lines, diseases, etc.). Read more about the benefits of NER tagging in What Are NER Concepts?
Let's say you have asked a series of questions about COVID-19, and would like to use the Vyasa NER annotations to see which concepts are most frequently mentioned regularly across multiple answers and their Evidence.
Go to "Filters"
Under "Show Top Related Concepts", select "Mentioned in multiple answers".
NER concepts that are mentioned in the Evidence for more than one answer node will become new nodes in the Knowledge Graph.
If you want to know which NER concepts are found multiple times in the Evidence for each answer (an indication that it is a primary focus for the article), select "Mentioned multiple times per answer".
Pro Tip: If you are only interested in a specific subset of NER concepts, such as proteins and disease, you can whittle down your results by going to the Concept filter (see below) and select the few concept types you are interested in.