I’m training an NER model to recognize a custom entity that is specific to my domain. I have a lot of example documents where the first word in the document is an example of the entity I’m after. However, in real data the entity is likely to appear anywhere in the document.
What features does the NER model use to detect entities? Does it use word position? Does it use word context? etc?
Basically, I want to avoid training a model that is biased towards selecting the first word(s) as being the entity that I am interested in.