I need to build semantic word embeddings representation of compound terms like "electronic engineer" or "microsoft excel". One approach would be to use a standard pretrained model an average the words but, since I have a corpus of my domain, is there a possible better approach?
To be more precise:
The data I have is a corpus of millions of documents. Each document is ~ half a page and contains these compound terms. However there may be compound terms not included in the corpus.