How can VIF avoid multicollinearity issues in a linear regression model?
Word2Vec uses a neural network to represent words whose hidden network encodes the representation into vectors. On the other hand, FastText breaks words into several n-grams and train on the data. For instance, the tri-grams for the word apple is app, ppl, and ple (ignoring the starting and ending of boundaries of words). The word embedding vector for apple will be the sum of all these n-grams. FastText takes longer time compared to Word2Vec but it performs better than Word2Vec in terms of embeddings
