NER with LSTM - How to recognize person names that are not part of the vocabulary?
With word-level vocabularies, unknown words are normally encoded with a special token &lt;unk&gt;. If in the training data there were enough examples of person names, then the bidirectional LSTM may have learned to identify the person name from the context, not just the word itself. In that case, and provided that in the specific input sentence there is enough context, the model may be able to identify it. If the model were a normal LSTM instead of a bidirectional one, it would be more difficult for the model to identify it, as it would only be able to use the context to the left of the unknown word.
If the training data did not contain such examples, the it won't be able to identify the person from the context.
One option to improve the handing of this problem  would be to force this kind of examples in the training data, by replacing person names with unknown words with certain probability.
Another option would be to use a subword-level vocabulary, like a byte-pair encoding (BPE) one, eliminating altogether the out-of-vocabulary (OOV) word problem.