I have a "problem" with NER labeling. I have a number of phrases for the same concept, something like
"US has decided"
"US administration has decided"
"White House has decided"
"Washington has decided" (often capitals names are used as representation for the country)
and a few more. If I consider each as a separate entity the # occurrences becomes very low, and NER training becomes difficult / converges slowly. It is possible to do something like this in spaCy, but is there something equivalent in Prodigy? I can parse the whole text with regex on these phrases and map them to the concept, but that's not very elegant.