Understanding Transfer Learning of Word Embeddings
So Named Entity Recognition is a mechanism where you ask your network to learn about how to detect entities given word vectors as the input.
The theoretical aspect of word embeddings is that based on your construction of sentences, the word embeddings for Orange and Apple are very similar i.e their cosine angle is very small.
In Named entity recognition you use these word embeddings and feed them into a network where the data you are training one has tags for each of the word embeddings i.e entities or normal words. So your network is actually understanding the relationship of the word embeddings and how to tag them. This makes it incredible for us to see that Apple gets detected even though its not in the training set, precisely where word embeddings help us quite well because the word embeddings are usually trained on a large corpus of data containing the words apple, orange and other tokens. This is where transfer learning helps because you are using the word embeddings trained in an unsupervised manner and then used to learn about entities.
Hope that helps. I can elaborate if required.