# Would averaging two vectors in word embeddings make sense?

I'm currently using the GloVe embedding matrix which is pre-trained on a large corpus. For my purpose it works fine, however, there are a few words which it does not know (for example, the word 'eSignature'). This spoils my results a bit. I do not have the time or data to retrain on a different (more domain-specific) corpus, so I wondered if I could add vectors based on existing vectors. By E(word) I denote the embedding of a word. Would the following work?

E(eSignature) = 1/2 * ( E(electronic) + E(signature) )


If not, what are other ideas that I could use to add just a few words in a word embedding?