Word embeddings are the staple of any Natural Language Processing (NLP) task. In fact, representation of words in the form of vectors is probably the first step in building any NLP application. These vector representations of words fall in a wide spectrum in semantic encoding space, with a one-hot representation on one end of the spectrum, encoding absolutely nothing in terms of semantics between words and the other end of the spectrum still being an active area of research with ELMo embeddin...
