Hands-On Guide To Word Embeddings Using GloVe

Creating representations of words is to capture their meaning, semantic relationship, and context of different words; here, different word embedding techniques play a role. A word embedding is an approach used to provide dense vector representation of words that capture some context words about their own. These are improved versions of simple bag-of-words models like word counts and frequency counters, mostly representing sparse vectors.  Word embeddings use an algorithm to train fixed-length dense vectors and continuous-valued vectors based on a large text corpus. Each word represents a point in vector space, and these points are learned and moved around the target word by preserving semantic relationships. The vector space representation of words provides a projection where words