I am working on the literature documents. I am able to identify important entities using NER and Ontologies. Now I will like to assign the relevance score to the identified entities with respect to the document. I need an approach to get to this, for relevancy I will also like to consider the indirect occurrence of entities Eg - In the document after first occurrence of the entity (by its name) it may be referred using terms like it, this, that ..etc. I tried frequency based approaches but not getting proper results.
Following is the example
Input text "In 2009, Jack Sparrow worked on NLP Api to process the written word, with all of its quirks and nuances, and got immediate traction. That first month, the company's eponymous language-analysis API processed 500,000 transactions. “Context is super-important,” he adds. “'I'm dying' is a lot different than 'I'm dying to buy the new iPhone.'” “As we move into new markets, we're going to be making some new hires," Jack says. "We knocked down some walls and added 2,000 square feet to our office.” Clients include Walmart, PR Newswire and numerous publishers and advertising networks. “This allows a news organization to detect what a person likes to read about,” says Sparrow of publishers and advertisers.."
Representational Entities and their scores – Jack Sparrow - 0.906712, PR Newswire - 0.292422, iPhone - 0.189069
Any help is appreciated..