I am learning Named Entity Recognition and going through posts similar to this one:
Named-Entity Recognition (NER) using Keras Bidirectional LSTM
So the sentences are fed into the model as a sequence of integers - every int corresponding to the index in the vocabulary - from what I understand this is how the embedding layer works.
My question is - does that mean the model would not be able to recognize a person's name if it doesn't exist in the vocabulary?
For example, from the sentence:
"John Doe went for a walk"
given John Doe is in the vocabulary, it will be recognized as a person name but the sentence:
"Unknown Name went for a walk"
will not be properly tagged if Unknown Name is not in the vocab?
To me, this would be a little strange as Unknown Name is in the same context as John Doe so I was hoping to be able to somehow tag it properly.
I'm obviously lacking knowledge here so I'd be very grateful for any suggestions and reference materials.