Data Science, Machine Learning, NLP, Word Embeddings, AI.
estimate those parameters well enough to build a reasonably generalisable model (see: curse of dimensionality). As a rough rule of thumb — you want orders of magnitude more training data than you have features. The computational issue. Each word’s embedding/feature vector is mostly zeroes, and many